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Artificial Intelligence Review

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29.01.2019

A review of unsupervised feature selection methods

verfasst von: Saúl Solorio-Fernández, J. Ariel Carrasco-Ochoa, José Fco. Martínez-Trinidad

Erschienen in: Artificial Intelligence Review | Ausgabe 2/2020

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Abstract

In recent years, unsupervised feature selection methods have raised considerable interest in many research areas; this is mainly due to their ability to identify and select relevant features without needing class label information. In this paper, we provide a comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature. We present a taxonomy of these methods and describe the main characteristics and the fundamental ideas they are based on. Additionally, we summarized the advantages and disadvantages of the general lines in which we have categorized the methods analyzed in this review. Moreover, an experimental comparison among the most representative methods of each approach is also presented. Finally, we discuss some important open challenges in this research area.

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Also called instances, observations or samples; commonly represented as vectors.

The set composed by the square of the singular values of the data matrix.

Clustering can be made using the Constrained Boolean Matrix Factorization (CBMF) algorithm proposed by Li et al. (2014a) or employing eigendecomposition and exhaustive search.

The number in parentheses denotes the number of datasets used for validation.

Unlike supervised feature selection, which has class labels to guide the search for discriminative features, in UFS, we must define feature relevancy in the form of objective concepts.

https://archive.ics.uci.edu/ml/index.php.

In order to get more reliable results, we repeat the k-means algorithm ten times with different initial points and report the average clustering quality results.

Agrawal S, Agrawal J (2015) Survey on anomaly detection using data mining techniques. Procedia Comput Sci 60(1):708–713. https://doi.org/10.1016/j.procs.2015.08.220 CrossRef

Ahmed M, Mahmood AN, Islam MR (2016) A survey of anomaly detection techniques in financial domain. Future Genera Comput Syst 55:278–288. https://doi.org/10.1016/j.future.2015.01.001 CrossRef

Alelyani S (2013) On feature selection stability: a data perspective. Arizona State University, Tempe

Alelyani S, Liu H, Wang L (2011) The effect of the characteristics of the dataset on the selection stability. In: Proceedings—international conference on tools with artificial intelligence, ICTAI, pp 970–977. https://doi.org/10.1109/ICTAI.2011.167

Alelyani S, Tang J, Liu H (2013) Feature selection for clustering: a review. Data Cluster Algorithms Appl 29:110–121

Alter O, Alter O (2000) Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA 97(18):10101–10106

Ambusaidi MA, He X, Nanda P (2015) Unsupervised feature selection method for intrusion detection system. In: Trustcom/BigDataSE/ISPA, 2015 IEEE, vol 1, pp 295–301. https://doi.org/10.1109/Trustcom.2015.387

Ang JC, Mirzal A, Haron H, Hamed HNA (2016) Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection. IEEE/ACM Trans Comput Biol Bioinform 13(5):971–989. https://doi.org/10.1109/TCBB.2015.2478454 CrossRef

Argyriou A, Evgeniou T, Pontil M (2008) Convex multi-task feature learning. Mach Learn 73(3):243–272

Banerjee M, Pal NR (2014) Feature selection with SVD entropy: some modification and extension. Inf Sci 264:118–134. https://doi.org/10.1016/j.ins.2013.12.029 MathSciNetCrossRefMATH

Beni G, Wang J (1993) Swarm intelligence in cellular robotic systems. In: Dario P, Sandini G, Aebischer P (eds) Robots and biological systems: towards a new bionics?. Springer, Berlin, pp 703–712. https://doi.org/10.1007/978-3-642-58069-7_38 CrossRef

Bharti KK, kumar Singh P (2014) A survey on filter techniques for feature selection in text mining. In: Proceedings of the second international conference on soft computing for problem solving (SocProS 2012), December 28–30, 2012. Springer, pp 1545–1559

Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2015) Feature selection for high-dimensional data. https://doi.org/10.1007/978-3-319-21858-8

Boyd S, Parikh N, Chu E, Peleato B, Eckstein J et al (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach® Learn 3(1):1–122MATH

Breaban M, Luchian H (2011) A unifying criterion for unsupervised clustering and feature selection. Pattern Recognit 44(4):854–865. https://doi.org/10.1016/j.patcog.2010.10.006 CrossRef

Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 333–342

Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective. Neurocomputing 0:1–10. https://doi.org/10.1016/j.neucom.2017.11.077 CrossRef

Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat Theory Methods 3(1):1–27. https://doi.org/10.1080/03610927408827101, http://www.tandfonline.com/doi/abs/10.1080/03610927408827101?journalCode=lsta19#preview MathSciNetMATH

Chakrabarti S, Frank E, Güting RH, Han J, Jiang X, Kamber M, Lightstone SS, Nadeau TP, Neapolitan RE et al (2008) Data mining: know it all. Elsevier Science. https://books.google.com.mx/books?id=WRqZ0QsdxKkC

Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28. https://doi.org/10.1016/j.compeleceng.2013.11.024 CrossRef

Chung FRK (1997) Spectral graph theory, vol 92. American Mathematical Society, ProvidenceMATH

Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATH

Cover TM, Thomas JA (2006) Elements of information theory, 2nd edn. Wiley, New YorkMATH

Dadaneh BZ, Markid HY, Zakerolhosseini A (2016) Unsupervised probabilistic feature selection using ant colony optimization. Expert Syst Appl 53:27–42. https://doi.org/10.1016/j.eswa.2016.01.021 CrossRef

Daniels MJ, Normand SLT (2005) Longitudinal profiling of health care units based on continuous and discrete patient outcomes. Biostatistics 7(1):1–15MATH

Dash M, Liu H (2000) Feature selection for Clustering. In: Terano T, Liu H, Chen ALP (eds) Knowledge discovery and data mining. Current issues and new applications, vol 1805, pp 110–121. https://doi.org/10.1007/3-540-45571-X_13

Dash M, Ong YS (2011) RELIEF-C: efficient feature selection for clustering over noisy data. In: 2011 23rd IEEE international conference on tools with artificial intelligence (ICTAI). IEEE, pp 869–872

Dash M, Liu H, Yao J (1997) Dimensionality reduction of unsupervised data. In: Proceedings Ninth IEEE international conference on tools with artificial intelligence. IEEE Computer Society, pp 532–539. https://doi.org/10.1109/TAI.1997.632300, http://ieeexplore.ieee.org/document/632300/

Dash M, Choi K, Scheuermann P, Liu HLH (2002) Feature selection for clustering—a filter solution. In: 2002 Proceedings 2002 IEEE international conference on data mining. pp 115–122. https://doi.org/10.1109/ICDM.2002.1183893

De Leon AR, Chough KC (2013) Analysis of mixed data: methods and applications. CRC Press, LondonMATH

Dempster AP, Laird NM, Rubin DB (1977) Maximum Likelihood from Incomplete Data via the EM-Alogrithm, vol 39. https://doi.org/10.2307/2984875, arXiv:0710.5696v2

Devakumari D, Thangavel K (2010) Unsupervised adaptive floating search feature selection based on Contribution Entropy. In: 2010 International conference on communication and computational intelligence (INCOCCI). IEEE, pp 623–627

Devaney M, Ram A (1997) Efficient feature selection in conceptual clustering. In: ICML ’97 Proceedings of the fourteenth international conference on machine learning. pp 92–97. Morgan Kaufmann Publishers Inc, San Francisco, CA. http://dl.acm.org/citation.cfm?id=645526.657124

Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Pattern recognition: a statistical approach. http://www.scopus.com/inward/record.url?eid=2-s2.0-0019926397&partnerID=40

Dong G, Liu H (2018) Feature engineering for machine learning and data analytics. CRC Press. https://books.google.com.au/books?hl=en&lr=&id=QmNRDwAAQBAJ&oi=fnd&pg=PT15&ots=4FR0a_rfAH&sig=xMBalldd_vLcQdcnDWy9q7c_z7c#v=onepage&q&f=false

Donoho DL, Tsaig Y (2008) Fast solution of-norm minimization problems when the solution may be sparse. IEEE Trans Inf Theory 54(11):4789–4812MathSciNetMATH

Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evolut Comput 1(1):53–66

Du S, Ma Y, Li S, Ma Y (2017) Robust unsupervised feature selection via matrix factorization. Neurocomputing 241:115–127. https://doi.org/10.1016/j.neucom.2017.02.034 CrossRef

Dutta D, Dutta P, Sil J (2014) Simultaneous feature selection and clustering with mixed features by multi objective genetic algorithm. Int J Hybrid Intell Syst 11(1):41–54

Dy JG, Brodley CE (2004) Feature selection for unsupervised learning. J Mach Learn Res 5:845–889. https://doi.org/10.1016/j.patrec.2014.11.006 MathSciNetCrossRefMATH

El Ghaoui L, Li GC, Duong VA, Pham V, Srivastava AN, Bhaduri K (2011) Sparse machine learning methods for understanding large text corpora. In: CIDU, pp 159–173

Feldman R, Sanger J (2006) The text mining handbook. Cambridge university press. https://doi.org/10.1017/CBO9780511546914, https://www.cambridge.org/core/product/identifier/9780511546914/type/book, arXiv:1011.1669v3

Ferreira AJ, Figueiredo MA (2012) An unsupervised approach to feature discretization and selection. Pattern Recognit 45(9):3048–3060. https://doi.org/10.1016/j.patcog.2011.12.008 CrossRef

Figueiredo MAT, Jain AK (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3):381–396. https://doi.org/10.1109/34.990138 CrossRef

Fisher DH (1987) Knowledge acquisition via incremental conceptual clustering. Mach Learn 2(2):139–172. https://doi.org/10.1023/A:1022852608280 CrossRef

Fix E, Hodges Jr JL (1951) Discriminatory analysis-nonparametric discrimination: consistency properties. Technical report. California University Berkeley

Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3:1289–1305MATH

Fowlkes EB, Gnanadesikan R, Kettenring JR (1988) Variable selection in clustering. J Classif 5(2):205–228. https://doi.org/10.1007/BF01897164 MathSciNetCrossRef

Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701. https://doi.org/10.1080/01621459.1937.10503522 CrossRefMATH

Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, 1st edn. Springer series in statistics. Springer, New YorkMATH

Fukunaga K (1990) Introduction to statistical pattern recognition, vol 22. https://doi.org/10.1016/0098-3004(96)00017-9, http://books.google.com/books?id=BIJZTGjTxBgC&pgis=1, arXiv:1011.1669v3

García S, Luengo J, Herrera F (2015) Data preprocessing in data mining, 72nd edn. Springer, New York. https://doi.org/10.1007/978-3-319-10247-4 CrossRef

Garcia-Garcia D, Santos-Rodriguez R (2009) Spectral clustering and feature selection for microarray data. In: International conference on machine learning and applications, 2009 ICMLA ’09 pp 425–428. https://doi.org/10.1109/ICMLA.2009.86

Gu S, Zhang L, Zuo W, Feng X (2014) Projective dictionary pair learning for pattern classification. In: Advances in neural information processing systems, pp 793–801

Guo J, Zhu W (2018) Dependence guided unsupervised feature selection. In: Aaai, pp 2232–2239

Guo J, Guo Y, Kong X, He R (2017) Unsupervised feature selection with ordinal locality school of information and communication engineering. Dalian University of Technology National, Laboratory of Pattern Recognition, CASIA Center for Excellence in Brain Science and Intelligence Technology, Dalian

Guyon I, Elisseeff A, De AM (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182. https://doi.org/10.1016/j.aca.2011.07.027, arXiv:1111.6189v1

Haindl M, Somol P, Ververidis D, Kotropoulos C (2006) Feature selection based on mutual correlation. In: Progress in pattern recognition, image analysis and applications, pp 569–577

Hall MA (1999) Correlation-based feature selection for machine learning. Ph.D. thesis, University of Waikato Hamilton

Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11(1):10–18. https://doi.org/10.1145/1656274.1656278 CrossRef

Han J, Sun Z, Hao H (2015) Selecting feature subset with sparsity and low redundancy for unsupervised learning. Knowl Based Syst 86:210–223. https://doi.org/10.1016/j.knosys.2015.06.008 CrossRef

He X, Niyogi P (2004) Locality preserving projections. In: Advances in neural information processing systems, pp 153–160

He X, Cai D, Niyogi P (2005) Laplacian score for feature selection. In: Advances in neural information processing systems 18, vol 186, pp 507–514

Hou C, Nie F, Yi D, Wu Y (2011) Feature selection via joint embedding learning and sparse regression. In: IJCAI Proceedings-international joint conference on artificial intelligence, Citeseer, vol 22. pp 1324

Hou C, Nie F, Li X, Yi D, Wu Y (2014) Joint embedding learning and sparse regression: a framework for unsupervised feature selection. IEEE Trans Cybern 44(6):793–804

Hruschka ER, Covoes TF (2005) Feature selection for cluster analysis: an approach based on the simplified Silhouette criterion. In: 2005 and international conference on intelligent agents, web technologies and internet commerce, international conference on computational intelligence for modelling, control and automation, vol 1. IEEE, pp 32–38

Hruschka ER, Hruschka ER, Covoes TF, Ebecken NFF (2005) Feature selection for clustering problems: a hybrid algorithm that iterates between k-means and a Bayesian filter. In: Fifth international conference on hybrid intelligent systems, 2005. HIS ’05. IEEE. https://doi.org/10.1109/ICHIS.2005.42

Hruschka ER, Covoes TF, Hruschka JER, Ebecken NFF (2007) Adapting supervised feature selection methods for clustering tasks. In: Methods for clustering tasks in managing worldwide operations and communications with information technology (IRMA 2007 proceedings), information resources management association (IRMA) international conference vancouver 2007 99-102 Hershey: Idea Group Publishing. https://doi.org/10.4018/978-1-59904-929-8.ch024

Hu J, Xiong C, Shu J, Zhou X, Zhu J (2009) An improved text clustering method based on hybrid model. Int J Modern Educ Comput Sci 1(1):35

Huang Z (1997) Clustering large data sets with mixed numeric and categorical values. In: Proceedings of the 1st Pacific-Asia conference on knowledge discovery and data mining,(PAKDD), Singapore. pp 21–34

Huang Z (1998) Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Discov 2(3):283–304

Jashki A, Makki M, Bagheri E, Ghorbani AA (2009) An iterative hybrid filter-wrapper approach to feature selection for document clustering. In: Proceedings of the 22nd Canadian conference on artificial intelligence (AI’09) 2009

John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the eleventh conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., pp 338–345

Kim Y, Gao J (2006) Unsupervised gene selection for high dimensional data. In: Sixth IEEE symposium on bioinformatics and bioengineering (BIBE’06), pp 227–234. https://doi.org/10.1109/BIBE.2006.253339, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4019664

Kim Y, Street WN, Menczer F (2002) Evolutionary model selection in unsupervised learning. Intell Data Anal 6(6):531–556MATH

Kong D, Ding C, Huang H (2011) Robust nonnegative matrix factorization using l21-norm. In: Proceedings of the 20th ACM international conference on Information and knowledge management (CIKM), pp 673–682. https://doi.org/10.1145/2063576.2063676, http://dl.acm.org/citation.cfm?id=2063676

Kotsiantis SB (2011) Feature selection for machine learning classification problems: a recent overview. Artifi Intell Rev 42:157–176. https://doi.org/10.1007/s10462-011-9230-1 CrossRef

Law MHC, Figueiredo MAT, Jain AK (2004) Simultaneous feature selection and clustering using mixture models. IEEE Trans Pattern Anal Mach Intell 26(9):1154–1166

Lazar C, Taminau J, Meganck S, Steenhoff D, Coletta A, Molter C, De Schaetzen V, Duque R, Bersini H, Nowé A (2012) A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE/ACM Trans Comput Biol Bioinform 9(4):1106–1119. https://doi.org/10.1109/TCBB.2012.33 CrossRef

Lee W, Stolfo SJ, Mok KW (2000) Adaptive intrusion detection: a data mining approach. Artif Intell Rev 14(6):533–567MATH

Lee PY, Loh WP, Chin JF (2017) Feature selection in multimedia: the state-of-the-art review. Image Vis Comput 67:29–42. https://doi.org/10.1016/j.imavis.2017.09.004 CrossRef

Li Z, Tang J (2015) Unsupervised feature selection via nonnegative spectral analysis and redundancy control. IEEE Trans Image Process 24(12):5343–5355. https://doi.org/10.1109/TIP.2015.2479560, http://ieeexplore.ieee.org/document/7271072/ MathSciNetMATH

Li Y, Lu BL, Wu ZF (2006) A hybrid method of unsupervised feature selection based on ranking. In: 18th international conference on pattern recognition (ICPR’06), Hong Kong, China, pp 687–690. https://doi.org/10.1109/ICPR.2006.84, http://dl.acm.org/citation.cfm?id=1172253

Li Y, Lu BL, Wu ZF (2007) Hierarchical fuzzy filter method for unsupervised feature selection. J Intell Fuzzy Syst 18(2):157–169. http://dl.acm.org/citation.cfm?id=1368376.1368381

Li Z, Yang Y, Liu J, Zhou X, Lu H (2012) Unsupervised feature selection using nonnegative spectral analysis. In: AAAI

Li Z, Cheong LF, Zhou SZ (2014a) SCAMS: Simultaneous clustering and model selection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 264–271. https://doi.org/10.1109/CVPR.2014.41

Li Z, Liu J, Yang Y, Zhou X, Lu H (2014b) Clustering-guided sparse structural learning for unsupervised feature selection. IEEE Trans Knowl Data Eng 26(9):2138–2150

Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H (2016) Feature selection: a data perspective. J Mach Learn Res 1–73. arXiv:1601.07996

Lichman M (2013) UCI Machine learning repository. http://archive.ics.uci.edu/ml

Liu H, Motoda H (1998) Feature selection for knowledge discovery and data mining. https://doi.org/10.1007/978-1-4615-5689-3, arXiv:1011.1669v3 MATH

Liu H, Motoda H (2007) Computational methods of feature selection. CRC Press, LondonMATH

Liu DC, Nocedal J (1989) On the limited memory BFGS method for large scale optimization. Math Program 45(1–3):503–528. https://doi.org/10.1007/BF01589116, arXiv:1011.1669v3 MathSciNetMATH

Liu H, Yu L, Member SS, Yu L, Member SS (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502. https://doi.org/10.1109/TKDE.2005.66 CrossRef

Liu J, Ji S, Ye J (2009a) Multi-task feature learning via efficient l 2, 1-norm minimization. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, pp 339–348

Liu R, Yang N, Ding X, Ma L (2009b) An unsupervised feature selection algorithm: Laplacian score combined with distance-based entropy measure. In: 3rd international symposium on intelligent information technology application, IITA 2009, vol 3, pp 65–68. https://doi.org/10.1109/IITA.2009.390

Liu H, Wei R, Jiang G (2013) A hybrid feature selection scheme for mixed attributes data. Comput Appl Math 32(1):145–161MathSciNetMATH

Lu Q, Li X, Dong Y (2018) Structure preserving unsupervised feature selection. Neurocomputing 301:36–45. https://doi.org/10.1016/j.neucom.2018.04.001 CrossRef

Luo Y, Xiong S (2009) Clustering ensemble for unsupervised feature selection. In: Fourth international conference on fuzzy systems and knowledge discovery. IEEE Computer Society, Los Alamitos, vol 1, pp 445–448. https://doi.org/10.1109/FSKD.2009.449

Luo M, Nie F, Chang X, Yang Y, Hauptmann AG, Zheng Q (2018) Adaptive unsupervised feature selection with structure regularization. IEEE Trans Neural Netw Learn Syst 29(4):944–956. https://doi.org/10.1109/TNNLS.2017.2650978, http://www.contrib.andrew.cmu.edu/~uqxchan1/papers/TNNLS2017_ANFS.pdf

Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416. https://doi.org/10.1007/s11222-007-9033-z, http://dl.acm.org/citation.cfm?id=1288832 MathSciNet

MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5-th Berkeley symposium on mathematical statistics and probability, vol 1, pp 281–297. http://projecteuclid.org/euclid.bsmsp/1200512992

Mao K (2005) Identifying critical variables of principal components for unsupervised feature selection. Syst Man Cybern Part B Cybern 35(2):339–44. https://doi.org/10.1109/TSMCB.2004.843269 MathSciNetCrossRef

Maron ME (1961) Automatic indexing: an experimental inquiry. J ACM 8(3):404–417. https://doi.org/10.1145/321075.321084, http://portal.acm.org/citation.cfm?doid=321075.321084 MATH

Miao J, Niu L (2016) A survey on feature selection. Procedia Comput Sci 91(Itqm):919–926. https://doi.org/10.1016/j.procs.2016.07.111 CrossRef

Mitra PFSUFS, Ca M, Pal SK (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intelligence 24(3):301–312. https://doi.org/10.1109/34.990133 CrossRef

Mugunthadevi K, Punitha SC, Punithavalli M (2011) Survey on feature selection in document clustering. Int J Comput Sci Eng 3(3):1240–1244. http://www.enggjournals.com/ijcse/doc/IJCSE11-03-03-077.pdf

Nie F, Huang H, Cai X, Ding CH (2010) Efficient and robust feature selection via joint 2, 1-norms minimization. In: Advances in neural information processing systems, pp 1813–1821

Nie F, Zhu W, Li X (2016) Unsupervised feature selection with structured graph optimization. In: Proceedings of the 30th conference on artificial intelligence (AAAI 2016), vol 13, No. 9, pp 1302–1308

Niijima S, Okuno Y (2009) Laplacian linear discriminant analysis approach to unsupervised feature selection. IEEE ACM Trans Comput Biol Bioinform 6(4):605–614. https://doi.org/10.1109/TCBB.2007.70257 CrossRef

Osborne MR, Presnell B, Turlach BA (2000) On the lasso and its dual. J Comput Graph Stat 9(2):319–337MathSciNet

Padungweang P, Lursinsap C, Sunat K (2009) Univariate filter technique for unsupervised feature selection using a new Laplacian score based local nearest neighbors. In: Asia-Pacific conference on information processing, 2009. APCIP 2009, vol 2. IEEE, pp 196–200

Pal SK, Mitra P (2004) Pattern Recognit Algorithms Data Min, 1st edn. Chapman and Hall/CRC, London

Pal SK, De RK, Basak J (2000) Unsupervised feature evaluation: a neuro-fuzzy approach. IEEE Trans Neural Netw 11(2):366–376

Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238. https://doi.org/10.1109/TPAMI.2005.159 CrossRef

Qian M, Zhai C (2013) Robust unsupervised feature selection. In: Proceedings of the twenty-third international joint conference on artificial intelligence, pp 1621–1627. http://dl.acm.org/citation.cfm?id=2540361

Rao VM, Sastry VN (2012) Unsupervised feature ranking based on representation entropy. In: 2012 1st international conference on recent advances in information technology, RAIT-2012, pp 421–425. https://doi.org/10.1109/RAIT.2012.6194631

Ritter G (2015) Robust cluster analysis and variable selection, vol 137. CRC Press, LondonMATH

Roth V, Lange T (2004) Feature selection in clustering problems. Adv Neural Inf Process Syst 16:473–480

Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science (New York, NY) 290(5500):2323–2326. https://doi.org/10.1126/science.290.5500.2323 CrossRef

Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517. https://doi.org/10.1093/bioinformatics/btm344 CrossRef

Sheikhpour R, Sarram MA, Gharaghani S, Chahooki MAZ (2017) A survey on semi-supervised feature selection methods. Pattern Recognit 64(2016):141–158. https://doi.org/10.1016/j.patcog.2016.11.003 CrossRefMATH

Shi L, Du L, Shen YD (2015) Robust spectral learning for unsupervised feature selection. In: Proceedings—IEEE international conference on data mining, ICDM 2015-Janua, pp 977–982. https://doi.org/10.1109/ICDM.2014.58

Shi Y, Miao J, Wang Z, Zhang P, Niu L (2018) Feature Selection With L2,1–2 Regularization. IEEE Trans Neural Netw Learn Syst 29(10):4967–4982. https://doi.org/10.1109/TNNLS.2017.2785403, https://ieeexplore.ieee.org/document/8259312/

Solorio-Fernández S, Carrasco-Ochoa J, Martínez-Trinidad J (2016) A new hybrid filterwrapper feature selection method for clustering based on ranking. Neurocomputing 214, https://doi.org/10.1016/j.neucom.2016.07.026

Solorio-Fernández S, Martínez-Trinidad JF, Carrasco-Ochoa JA (2017) A new unsupervised spectral feature selection method for mixed data: a filter approach. Pattern Recognit 72:314–326. https://doi.org/10.1016/j.patcog.2017.07.020 CrossRef

Swets D, Weng J (1995) Efficient content-based image retrieval using automatic feature selection. Proceedings, international symposium on computer vision, 1995. pp 85–90, http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=476982

Tabakhi S, Moradi P (2015) Relevance-redundancy feature selection based on ant colony optimization. Pattern Recognit 48(9):2798–2811. https://doi.org/10.1016/j.patcog.2015.03.020 CrossRef

Tabakhi S, Moradi P, Akhlaghian F (2014) An unsupervised feature selection algorithm based on ant colony optimization. Eng Appl Artif Intell 32:112–123. https://doi.org/10.1016/j.engappai.2014.03.007 CrossRef

Tabakhi S, Najafi A, Ranjbar R, Moradi P (2015) Gene selection for microarray data classification using a novel ant colony optimization. Neurocomputing 168:1024–1036. https://doi.org/10.1016/j.neucom.2015.05.022 CrossRef

Talavera L (2000) Dependency-based feature selection for clustering symbolic data. Intell Data Anal 4:19–28MATH

Tang J, Liu H (2014) An unsupervised feature selection framework for social media data. IEEE Trans Knowl Data Eng 26(12):2914–2927

Tang J, Alelyani S, Liu H (2014) Feature selection for classification: a review. In: Data Classification, CRC Press, pp 37–64. https://doi.org/10.1201/b17320

Tang C, Liu X, Li M, Wang P, Chen J, Wang L, Li W (2018a) Robust unsupervised feature selection via dual self-representation and manifold regularization. Knowl Based Syst 145:109–120. https://doi.org/10.1016/j.knosys.2018.01.009 CrossRef

Tang C, Zhu X, Chen J, Wang P, Liu X, Tian J (2018b) Robust graph regularized unsupervised feature selection. Expert Syst Appl 96:64–76. https://doi.org/10.1016/j.eswa.2017.11.053 CrossRef

Theodoridis S, Koutroumbas K (2008a) Pattern recognition. Elsevier Science. https://books.google.com.mx/books?id=QgD-3Tcj8DkC

Theodoridis S, Koutroumbas K (2008b) Pattern recognition, 4th edn. Academic Press, New YorkMATH

Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodological) 58:267–288MathSciNetMATH

Tou JT, González RC (1974) Pattern recognition principles. Addison-Wesley Pub. Co. https://books.google.com/books?id=VWQoAQAAIAAJ

Varshavsky R, Gottlieb A, Linial M, Horn D (2006) Novel unsupervised feature filtering of biological data. Bioinformatics 22(14):e507–e513. https://doi.org/10.1093/bioinformatics/btl214, http://bioinformatics.oxfordjournals.org/content/22/14/e507.abstract

Vergara JR, Estévez PA (2014) A review of feature selection methods based on mutual information. Neural Comput Appl 24(1):175–186, https://doi.org/10.1007/s00521-013-1368-0, arXiv:1509.07577

Wang S, Wang H (2017) Unsupervised feature selection via low-rank approximation and structure learning. Knowl Based Syst 124:70–79. https://doi.org/10.1016/j.knosys.2017.03.002 CrossRef

Wang S, Pedrycz W, Zhu Q, Zhu W (2015a) Unsupervised feature selection via maximum projection and minimum redundancy. Knowl Based Syst 75:19–29. https://doi.org/10.1016/j.knosys.2014.11.008 CrossRefMATH

Wang S, Tang J, Liu H (2015b) Embedded unsupervised feature selection. In: Twenty-ninth AAAI conference on artificial intelligence, p 7

Wang X, Zhang X, Zeng Z, Wu Q, Zhang J (2016) Unsupervised spectral feature selection with l1-norm graph. Neurocomputing 200:47–54. https://doi.org/10.1016/j.neucom.2016.03.017 CrossRef

Webb AR (2003) Statistical pattern recognition, vol 35, 2nd edn. Wliey, New York. https://doi.org/10.1137/1035031 CrossRefMATH

Wu M, Schölkopf B (2007) A local learning approach for clustering. In: Advances in neural information processing systems, pp 1529–1536

Yang Y, Liao Y, Meng G, Lee J (2011a) A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis. Expert Syst Appl 38(9):11311–11320. http://dblp.uni-trier.de/db/journals/eswa/eswa38.html#YangLML11

Yang Y, Shen HT, Ma Z, Huang Z, Zhou X (2011b) L2,1-Norm regularized discriminative feature selection for unsupervised learning. In: IJCAI international joint conference on artificial intelligence, pp 1589–1594. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-267

Yasmin M, Mohsin S, Sharif M (2014) Intelligent image retrieval techniques: a survey. J Appl Res Technology 12(1):87–103

Yen CC, Chen LC, Lin SD (2010) Unsupervised feature selection: minimize information redundancy of features. In: Proceedings—international conference on technologies and applications of artificial intelligence, TAAI 2010. pp 247–254. https://doi.org/10.1109/TAAI.2010.49

Yi Y, Zhou W, Cao Y, Liu Q, Wang J (2016) Unsupervised feature selection with graph regularized nonnegative self-representation. In: You Z, Zhou J, Wang Y, Sun Z, Shan S, Zheng W, Feng J, Zhao Q (eds) Biometric recognition: 11th Chinese conference, CCBR 2016, Chengdu, China, October 14–16, 2016, Proceedings. Springer International Publishing, Cham, pp 591–599. https://doi.org/10.1007/978-3-319-46654-5_65

Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502

Yu J (2011) A hybrid feature selection scheme and self-organizing map model for machine health assessment. Appl Soft Comput 11(5):4041–4054

Zafarani R, Abbasi MA, Liu H (2014) Social media mining: an introduction. Cambridge University Press, Cambridge

Zeng H, Cheung YM (2011) Feature selection and kernel learning for local learning-based clustering. IEEE Trans Pattern Anal Mach Intell 33(8):1532–1547. https://doi.org/10.1109/TPAMI.2010.215 CrossRef

Zhao Z (2010) Spectral feature selection for mining ultrahigh dimensional data. Ph.d thesis, Tempe

Zhao Z, Liu H (2007) Spectral feature selection for supervised and unsupervised learning. In: Proceedings of the 24th international conference on machine learning. ACM, pp 1151–1157

Zhao Z, Liu H (2011) Spectral feature selection for data mining. CRC Press. pp 1–216. https://www.taylorfrancis.com/books/9781439862100

Zhao Z, Wang L, Liu H, Ye J (2013) On similarity preserving feature selection. IEEE Trans Knowl Data Eng 25(3):619–632. https://doi.org/10.1109/TKDE.2011.222, http://ieeexplore.ieee.org.proxy.lib.umich.edu/ielx5/69/6419729/06051436.pdf?tp=&arnumber=6051436&isnumber=6419729

Zheng Z, Lei W, Huan L (2010) Efficient spectral feature selection with minimum redundancy. In: Twenty-fourth AAAI conference on artificial intelligence, pp 1–6

Zhou W, Wu C, Yi Y, Luo G (2017) Structure preserving non-negative feature self-representation for unsupervised feature selection. IEEE Access 5:8792–8803. https://doi.org/10.1109/ACCESS.2017.2699741 CrossRef

Zhu P, Zuo W, Zhang L, Hu Q, Shiu SCK (2015) Unsupervised feature selection by regularized self-representation. Pattern Recognit 48(2):438–446MATH

Zhu P, Hu Q, Zhang C, Zuo W (2016) Coupled dictionary learning for unsupervised feature selection. In: AAAI, pp 2422–2428

Zhu P, Zhu W, Wang W, Zuo W, Hu Q (2017) Non-convex regularized self-representation for unsupervised feature selection. Image Vis Comput 60:22–29. https://doi.org/10.1016/j.imavis.2016.11.014 CrossRef

Titel: A review of unsupervised feature selection methods
verfasst von: Saúl Solorio-Fernández
J. Ariel Carrasco-Ochoa
José Fco. Martínez-Trinidad
Publikationsdatum: 29.01.2019
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 2/2020
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-019-09682-y

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