Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2020

09.07.2020

An Efficient Algorithm Combining Spectral Clustering with Feature Selection

verfasst von: Qimin Luo, Guoqiu Wen, Leyuan Zhang, Mengmeng Zhan

Erschienen in: Neural Processing Letters | Ausgabe 3/2020

Einloggen

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

search-config
loading …

Abstract

Traditional clustering algorithms have some limitations, which are sensitive to noise and mostly applicable to convex data sets. To solve these problems, the paper proposes a novel algorithm combining spectral clustering with feature selection. Specifically, the loss item is marked with a root that can reduce the deviation value then improve the robustness of the model. And in the algorithm optimization, there is one parameter is represented by other known parameters, which can reduce the time of parameter adjustment. Then, the regular term \({{\ell }_{2,p}}\text {-norm}\) is applied to reduce the influence of noise and redundant features and prevent the model from overfitting. Finally, Laplace matrix is constructed by kNN algorithm which is used to learn subspace and to preserve the local structure among samples, and the data after dimension reduction is used to spectral clustering. Experimental analysis on 10 benchmark datasets show that the proposed algorithm is more outperformed than the algorithms of the state-of-the-art.
Vorheriger Artikel A Style-Specific Music Composition Neural Network
Nächster Artikel Parameter-Free Extreme Learning Machine for Imbalanced Classification

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

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

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"

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
Literatur
1.
Chaudhari S, Cabric D (2017) Unsupervised frequency clustering algorithm for null space estimation in wideband spectrum sharing networks. In: 2017 IEEE global conference on signal and information processing, pp 224–228
2.
Elhamifar E, Vidal R (2013) Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans Pattern Anal Mach Intell 35(11):2765–2781CrossRef
3.
Gao L, Li X, Song J, Shen HT (2019) Hierarchical LSTMS with adaptive attention for visual captioning. IEEE Trans Pattern Anal Mach Intell
4.
Guo G, Wang H, Bell D, Bi Y, Greer K (2003) Knn model-based approach in classification. In: OTM confederated international conferences “on the move to meaningful internet systems”, pp 986–996
5.
Guo Y, Wu Z, Shen D (2019) Learning longitudinal classification-regression model for infant hippocampus segmentation. Neurocomputing
6.
7.
Hao S, Zhou Y, Guo Y (2020) A brief survey on semantic segmentation with deep learning. Neurocomputing
8.
Hartigan JA, Wong MA (1979) Algorithm as 136: a k-means clustering algorithm. J R Stat Soc Ser C (Applied Statistics) 28(1):100–108MATH
9.
Hou C, Nie F, Li X, Yi D, Wu Y (2013) Joint embedding learning and sparse regression: a framework for unsupervised feature selection. IEEE Trans Cybern 44(6):793–804
10.
11.
Huang D, Wang CD, Wu J, Lai JH, Kwoh CK (2019) Ultra-scalable spectral clustering and ensemble clustering. IEEE Trans Knowl Data Eng
12.
Jain AK, Murty MN, Flynn PJ (1999) Data clustering:a review. ACM Comput Surv 31(3):264–323CrossRef
13.
Ji Y, Zhan Y, Yang Y, Xu X, Shen F, Shen HT (2019) A context knowledge map guided coarse-to-fine action recognition. IEEE Trans Image Process
14.
Jia H, Ding S, Ma H, Xing W (2014) Spectral clustering with neighborhood attribute reduction based on information entropy. J Comput 9(6):1316–1324CrossRef
15.
Jia H, Ding S, Xu X, Nie R (2014) The latest research progress on spectral clustering. Neural Comput Appl 24(7–8):1477–1486CrossRef
16.
Kannan SR, Sathya A, Sathya A, Sathya A (2011) Robust kernel FCM in segmentation of breast medical images. Expert Syst Appl Int J 38(4):4382–4389CrossRef
17.
Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22(13):1658–1659CrossRef
18.
Li Z, Zhang Z, Qin J, Zhang Z, Shao L (2019) Discriminative fisher embedding dictionary learning algorithm for object recognition. IEEE Trans Neural Netw Learn Syst
19.
Liu Y, Jiao L, Shang F (2013) An efficient matrix factorization based low-rank representation for subspace clustering. Pattern Recognit 46(1):284–292CrossRef
20.
Lucinska M (2014) A spectral clustering algorithm based on eigenvector localization. In: International conference on artificial intelligence and soft computing. Springer, Berlin, pp 749–759
21.
Malik J, Belongie S, Leung T, Shi J (2001) Contour and texture analysis for image segmentation. Int J Comput Vis 43(1):7–27CrossRef
22.
Nie F, Wang CL, Li X (2019) K-multiple-means: a multiple-means clustering method with specified k clusters. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 959–967
23.
Nie F, Wang X, Jordan MI, Huang H (2016) The constrained laplacian rank algorithm for graph-based clustering. In: Proceedings of the thirtieth conference on artificial intelligence, pp 1969–1976
24.
Nie F, Zhang R, Li X (2017) A generalized power iteration method for solving quadratic problem on the stiefel manifold. Sci China Inf Sci 60(11):112101MathSciNetCrossRef
25.
Shen HT, Liu L, Yang Y, Xu X, Huang Z, Shen F, Hong R (2020) Exploiting subspace relation in semantic labels for cross-modal hashing. IEEE Trans Knowl Data Eng
26.
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRef
27.
Tremblay N, Puy G, Gribonval R, Vandergheynst P (2016) Compressive spectral clustering. In: International conference on machine learning, pp 1002–1011
28.
Wang B, Yang Y, Xu X, Hanjalic A, Shen HT (2017) Adversarial cross-modal retrieval. In: Proceedings of the 25th ACM international conference on multimedia, pp 154–162
29.
Weiss Y (1999) Segmentation using eigenvectors: a unifying view. In: Proceedings of the seventh IEEE international conference on computer vision, vol 2. IEEE, pp 975–982
30.
Xie G, Zhang Z, Liu L, Zhu F, Zhang X, Shao L, Li X (2019) SRSC: selective, robust, and supervised constrained feature representation for image classification. IEEE Trans Neural Netw Learn Syst (2019)
31.
Yang Y, Duan Y, Wang X, Huang Z, Xie N, Shen HT (2018) Hierarchical multi-clue modelling for poi popularity prediction with heterogeneous tourist information. IEEE Trans Knowl Data Eng 31(4):757–768CrossRef
32.
Yang Y, Ma Z, Yang Y, Nie F, Shen HT (2014) Multitask spectral clustering by exploring intertask correlation. IEEE Trans Cybern 45(5):1083–1094CrossRef
33.
Yang Y, Rutayisire T, Lin C, Li T, Teng F (2013) An improved cop-kmeans clustering for solving constraint violation based on mapreduce framework. Fundam Inform 126(4):301–318MathSciNetCrossRef
34.
Yang Y, Shen HT, Ma Z, Huang Z, Zhou X (2011) L2, 1 norm regularized discriminative feature selection for unsupervised learning. In: Proceedings of the 22nd international joint conference on artificial intelligence, pp 1589–1594
35.
Zhang J, Yu J, Wan J, Zeng Z (2015) L2, 1 norm regularized fisher criterion for optimal feature selection. Neurocomputing 166:455–463CrossRef
36.
Zhang Y, Jin J, Qing X, Wang B, Wang X (2012) Lasso based stimulus frequency recognition model for ssvep bcis. Biomed Signal Process Control 7(2):104–111CrossRef
37.
Zhang Y, Zhao Q, Jin J, Wang X, Cichocki A (2012) A novel BCI based on erp components sensitive to configural processing of human faces. J Neural Eng 9(2):26018CrossRef
38.
Zhang Z, Lai Z, Huang Z, Wong WK, Xie GS, Liu L, Shao L (2019) Scalable supervised asymmetric hashing with semantic and latent factor embedding. IEEE Trans Image Process 28(10):4803–4818MathSciNetCrossRef
39.
Zhang Z, Liu L, Shen F, Shen HT, Shao L (2018) Binary multi-view clustering. IEEE Trans Pattern Anal Mach Intell 41(7):1774–1782CrossRef
40.
Zhou Y, Tian L, Zhu C, Jin X, Sun Y (2019) Video coding optimization for virtual reality 360-degree source. IEEE J Sel Topi Signal Process
41.
Zhu X, Gan J, Lu G, Li J, Zhang S (2019) Spectral clustering via half-quadratic optimization. World Wide Web, pp 1–20
42.
43.
Zhu X, Zhang S, Zhu Y, Zheng W (2020) Self-weighted multi-view fuzzy clustering. In: ACM transactions on knowledge discovery from data (TKDD)
44.
Metadaten
Titel
An Efficient Algorithm Combining Spectral Clustering with Feature Selection
verfasst von
Qimin Luo
Guoqiu Wen
Leyuan Zhang
Mengmeng Zhan
Publikationsdatum
09.07.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10297-6

Weitere Artikel der Ausgabe 3/2020

Neural Processing Letters 3/2020 Zur Ausgabe

OriginalPaper

Local Structure Preservation for Nonlinear Clustering

OriginalPaper

Robust Biometrics from Motion Wearable Sensors Using a D-vector Approach

OriginalPaper

Ensemble pruning of ELM via migratory binary glowworm swarm optimization and margin distance minimization

OriginalPaper

Bipartite Synchronization Analysis of Fractional Order Coupled Neural Networks with Hybrid Control

OriginalPaper

Global Stabilization of Memristive Neural Networks with Leakage and Time-Varying Delays Via Quantized Sliding-Mode Controller

OriginalPaper

Parameter-Free Extreme Learning Machine for Imbalanced Classification

Neuer Inhalt

    Bildnachweise