nach oben

Neural Computing and Applications

Erschienen in:

28.08.2015 | Original Article

Supervised learning of sparse context reconstruction coefficients for data representation and classification

verfasst von: Xuejie Liu, Jingbin Wang, Ming Yin, Benjamin Edwards, Peijuan Xu

Erschienen in: Neural Computing and Applications | Ausgabe 1/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Context of data points, which is usually defined as the other data points in a data set, has been found to paly important roles in data representation and classification. In this paper, we study the problem of using context of a data point for its classification problem. Our work is inspired by the observation that actually only very few data points are critical in the context of a data point for its representation and classification. We propose to represent a data point as the sparse linear combination of its context and learn the sparse context in a supervised way to increase its discriminative ability. To this end, we proposed a novel formulation for context learning, by modeling the learning of context parameter and classifier in a unified objective, and optimizing it with an alternative strategy in an iterative algorithm. Experiments on three benchmark data set show its advantage over state-of-the-art context-based data representation and classification methods.

Vorheriger Artikel Moisture damage evaluation in SBS and lime modified asphalt using AFM and artificial intelligence

Nächster Artikel Adaptive NN prescribed performance control for nonlinear systems with output dead zone

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Agerri R, Artola X, Beloki Z, Rigau G, Soroa A (2015) Big data for natural language processing: a streaming approach. Knowl Based Syst 79:36–42CrossRef

Ahn WH, Nah SP, Seo BS (2015) Automatic classification of digitally modulated signals based on k-nearest neighbor. Lect Notes Electr Eng 329:63–69CrossRef

Aldea R, Fira M, Lazar A (2014) Classifications of motor imagery tasks using k-nearest neighbors. In: 2014 12th Symposium on neural network applications in electrical engineering (NEUREL). IEEE, pp 115–120

Deng Q, Cai A (2009) Svm-based loss differentiation mechanism in mobile ad hoc networks. In: 2009 Global Mobile Congress. GMC 2009. doi:10.1109/GMC.2009.5295834

Dessí N, Pes B (2015) Similarity of feature selection methods: an empirical study across data intensive classification tasks. Expert Syst Appl 42(10):4632–4642CrossRef

Duan Y, Stien L, Thorsen A, Karlsen Ø, Sandlund N, Li D, Fu Z, Meier S (2015) An automatic counting system for transparent pelagic fish eggs based on computer vision. Aquac Eng 67:8–13CrossRef

Feng Q, Pan T, Pan J, Tang L (2015) Improved mean representation based classification for face recognition. Lect Notes Electr Eng 330:1407–1412CrossRef

Gao S, Tsang IH, Chia LT (2013) Laplacian sparse coding, hypergraph laplacian sparse coding, and applications. IEEE Trans Pattern Anal Mach Intell 35(1):92–104CrossRef

Gao S, Tsang IW, Chia LT, Zhao P (2010) Local features are not lonely—Laplacian sparse coding for image classification. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3555–3561

10.

Garanina N, Sidorova E (2015) Ontology population as algebraic information system processing based on multi-agent natural language text analysis algorithms. Program Comput Softw 41(3):140–148MathSciNetCrossRefMATH

11.

Greving H, Sassenberg K (2015) Counter-regulation online: threat biases retrieval of information during internet search. Comput Hum Behav 50:291–298CrossRef

12.

Guerreiro A, Souza J, Rufino J (2014) Improving ns-2 network simulator to evaluate IEEE 802.15.4 wireless networks under error conditions. pp 213–220

13.

Guo Z, Li Q, You J, Zhang D, Liu W (2012) Local directional derivative pattern for rotation invariant texture classification. Neural Comput Appl 21(8):1893–1904CrossRef

14.

He Y, Sang N (2013) Multi-ring local binary patterns for rotation invariant texture classification. Neural Comput Appl 22(3–4):793–802CrossRef

15.

Huang F, Li C, Lin L (2014) Identifying gender of microblog users based on message mining. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 8485. LNCS, pp 488–493

16.

Jasiewicz J, Netzel P, Stepinski T (2015) Geopat: a toolbox for pattern-based information retrieval from large geospatial databases. Comput Geosci 80:62–73CrossRef

17.

Jin C, Jin SW (2015) Automatic image annotation using feature selection based on improving quantum particle swarm optimization. Signal Process 109:172–181CrossRef

18.

Kang M, Kim J, Kim JM, Tan A, Kim E, Choi BK (2015) Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis. IEEE Trans Power Electron 30(5):2786–2797CrossRef

19.

Karad A, Joshi R (2015) Rule based chunk extraction from PDF documents using regular expressions and natural language processing. Int J Appl Eng Res 10(3):7721–7726

20.

Kim S, Yu Z, Kil R, Lee M (2015) Deep learning of support vector machines with class probability output networks. Neural Netw 64:19–28CrossRefMATH

21.

Li H, Federico M, He X, Meng H, Trancoso I (2015) Introduction to the special section on continuous space and related methods in natural language processing. IEEE Trans Audio Speech Lang Process 23(3):427–430CrossRef

22.

Li Z, Gong D, Li X, Tao D (2015) Learning compact feature descriptor and adaptive matching framework for face recognition. IEEE Trans Image Process 24(9):2736–2745MathSciNetCrossRef

23.

Liu T, Wang G, Wang L, Chan K (2015) Visual tracking via temporally smooth sparse coding. IEEE Signal Process Lett 22(9):1452–1456CrossRef

24.

Lu J, Liong V, Wang G, Moulin P (2015) Joint feature learning for face recognition. IEEE Trans Inf Forensics Secur 10(7):1371–1383CrossRef

25.

Melacci S, Belkin M (2011) Laplacian support vector machines trained in the primal. J Mach Learn Res 12:1149–1184MathSciNetMATH

26.

Nayak M, Nayak A (2015) Odia running text recognition using moment-based feature extraction and mean distance classification technique. In: Advances in intelligent systems and computing 309 AISC, vol 2, pp 497–506

27.

Pouria Z, Mathews E, Havinga P, Stojanovski S, Sisinni E, Ferrari P (2014) Implementation of wirelesshart in the ns-2 simulator and validation of its correctness. Sensors (Switzerland) 14(5):8633–8668CrossRef

28.

Sheydaei N, Saraee M, Shahgholian A (2015) A novel feature selection method for text classification using association rules and clustering. J Inf Sci 41(1):3–15CrossRef

29.

Staglianó A, Noceti N, Verri A, Odone F (2015) Online space-variant background modeling with sparse coding. IEEE Trans Image Process 24(8):2415–2428MathSciNetCrossRef

30.

Tian Y, Zhang Q, Liu D (2014) v-Nonparallel support vector machine for pattern classification. Neural Comput Appl 25(5):1007–1020CrossRef

31.

Tomé A, Kuipers M, Pinheiro T, Nunes M, Heitor T (2015) Space-use analysis through computer vision. Autom Constr 57:80–97CrossRef

32.

Wang H, Wang J (2014) An effective image representation method using kernel classification. In: 2014 IEEE 26th international conference on tools with artificial intelligence (ICTAI), pp 853–858

33.

Wang J, Gao X, Wang Q, Li Y (2013) Prodis-contshc: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval. BMC Bioinform 13(Suppl 7):S2CrossRef

34.

Wang J, Li Y, Wang Q, You X, Man J, Wang C, Gao X (2012) Proclusensem: predicting membrane protein types by fusing different modes of pseudo amino acid composition. Comput Biol Med 42(5):564–574CrossRef

35.

Wang JJY, Bensmail H, Gao X (2012) Multiple graph regularized protein domain ranking. BMC Bioinform 13(1):307CrossRef

36.

Wang JJY, Bensmail H, Gao X (2013) Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification. Pattern Recognit 46(12):3249–3255CrossRef

37.

Wang JJY, Bensmail H, Gao X (2014) Feature selection and multi-kernel learning for sparse representation on a manifold. Neural Netw 51:9–16CrossRefMATH

38.

Wang JJY, Bensmail H, Yao N, Gao X (2013) Discriminative sparse coding on multi-manifolds. Knowl Based Syst 54:199–206CrossRef

39.

Wang JJY, Gao X (2014) Semi-supervised sparse coding. In: Proceedings of the international joint conference on neural networks, pp 1630–1637

40.

Wang JJY, Gao X (2015) Max–min distance nonnegative matrix factorization. Neural Netw 61:75–84CrossRefMATH

41.

Wang JJY, Sun Y, Gao X (2014) Sparse structure regularized ranking. Multimed Tools Appl 74(2):635–654CrossRef

42.

Wang JJY, Wang X, Gao X (2013) Non-negative matrix factorization by maximizing correntropy for cancer clustering. BMC Bioinform 14:107CrossRef

43.

Wang JJY, Wang Y, Jing BY, Gao X (2015) Regularized maximum correntropy machine. Neurocomputing 160:85–92CrossRef

44.

Wang JJY, Wang Y, Zhao S, Gao X (2015) Maximum mutual information regularized classification. Eng Appl Artif Intell 37:1–8CrossRef

45.

Wang JY, Almasri I, Gao X (2012) Adaptive graph regularized nonnegative matrix factorization via feature selection. In: Proceedings—international conference on pattern recognition, pp 963–966

46.

Wang JY, Almasri I, Shi Y, Gao X (2014) Semi-supervised transductive hot spot predictor working on multiple assumptions. Curr Bioinform 9(3):258–267CrossRef

47.

Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRef

48.

Xu Y, Fang X, You J, Chen Y, Liu H (2015) Noise-free representation based classification and face recognition experiments. Neurocomputing 147(1):307–314CrossRef

49.

Xu Y, Shen F, Zhao J (2012) An incremental learning vector quantization algorithm for pattern classification. Neural Comput Appl 21(6):1205–1215CrossRef

50.

Zhao S, Hu ZP (2015) A modular weighted sparse representation based on fisher discriminant and sparse residual for face recognition with occlusion. Inf Process Lett 115(9):677–683MathSciNetCrossRefMATH

Titel: Supervised learning of sparse context reconstruction coefficients for data representation and classification
verfasst von: Xuejie Liu
Jingbin Wang
Ming Yin
Benjamin Edwards
Peijuan Xu
Publikationsdatum: 28.08.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-2042-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 1/2017

A study of fuzzy membership functions for dependence decision-making in security robot system

Hesitant triangular multiplicative aggregation operators and their application to multiple attribute group decision making

Adaptive NN prescribed performance control for nonlinear systems with output dead zone

A study of the fixed points and spurious solutions of the deflation-based FastICA algorithm

An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome

Automatic general-purpose neural hardware generator