L1 graph based on sparse coding for feature selection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7951 LNCS, Issue PART 1, 2013, Pages 594-601

  Xu, Jin ^a   Yang, Guang ^b   Man, Hong ^b   He, Haibo ^c  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b Stevens Institute of Technology   (United States)

^c University of Rhode Island   (United States)

Author keywords

Feature selection; Neural network; Sparse coding; Unsupervised learning

Indexed keywords

CORRELATION METHODS; FEEDFORWARD NEURAL NETWORKS; GRAPH THEORY; GRAPHIC METHODS; NEAREST NEIGHBOR SEARCH; NETWORK LAYERS; NEURAL NETWORKS; SUPPORT VECTOR MACHINES; UNSUPERVISED LEARNING;

CLASSIFICATION RESULTS; GEOMETRY PROPERTIES; K-NEAREST NEIGHBORS; MULTI-LAYER FEED-FORWARD NETWORKS; PEARSON CORRELATION; SPARSE CODING; SUPERVISED METHODS; UNSUPERVISED FEATURE SELECTION;

FEATURE EXTRACTION;

EID: 84880715885     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-39065-4_71     Document Type: Conference Paper

References (14)

1. Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 301-312 (2002) (Pubitemid 35289348)
2. Mutch, J., Lowe, D.G.: Multiclass object recognition with sparse, localized features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 11-18 (2006) (Pubitemid 44931307)
3. Xu, J., He, H., Man, H.: DCPE Co-Training for Classification. Neurocomputing 86, 75-85 (2012)
4. Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing 15(12), 3736-3745 (2006) (Pubitemid 44811686)
5. Xu, J., Yin, Y., Man, H., He, H.: Feature selection based on sparse imputation. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1-7 (2012)
6. Xu, J., Man, H.: Dictionary Learning Based on Laplacian Score in Sparse Coding. In: Perner, P. (ed.) MLDM 2011. LNCS, vol. 6871, pp. 253-264. Springer, Heidelberg (2011)
7. Li, Y., Amari, S., Cichocki, A., Ho, D.W.C., Xie, S.: Underdetermined blind source separation based on sparse representation. IEEE Transactions on Signal Processing 54(2), 423-437 (2006) (Pubitemid 43141892)
8. Chung, F.R.K.: Spectral Graph Theory. Regional Conference Series in Mathematics, vol. 92 (1997)
9. Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2010), http://archive.ics.uci.edu/ml
10. Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157-1182 (2003)
11. He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: Proc. Advances in the Neural Information Processing Systems 18, Vancouver, Canada (2005)
12. Candes, E., Tao, T.: Near optimal signal recovery from random projections and universal encoding strategies. IEEE Trans. Inform. Theory 52, 5406-5425 (2006) (Pubitemid 46445381)
13. Kim, S., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for largescale l1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing 1(4), 606-617 (2007) (Pubitemid 351266626)
14. Elhamifar, E., Vidal, R.: Sparse Subspace Clustering. In: IEEE International Conference on Computer Vision and Pattern Recognition (2009)