Topographic NMF for data representation

IEEE Transactions on Cybernetics

Volumn 44, Issue 10, 2014, Pages 1762-1771

  Xiao, Yanhui ^a   Zhu, Zhenfeng ^a   Zhao, Yao ^a   Wei, Yunchao ^a   Wei, Shikui ^a   Li, Xuelong ^b  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

Data clustering; dimension reduction; feature invariance; machine learning; nonnegative matrix factorization

Indexed keywords

DATA CLUSTERING; DATA REPRESENTATIONS; DIMENSION REDUCTION; NONNEGATIVE MATRIX FACTORIZATION;

LEARNING SYSTEMS;

EID: 84907212739     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCYB.2013.2294215     Document Type: Article

References (34)

1. Y. Sun, J. Paik, A. Koschan, D. L. Page, and M. A. Abidi, "Point fingerprint: A new 3-D object representation scheme, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 33, no. 4, pp. 712-717, Aug. 2003.
2. K. Anderson and P. W. McOwan, "A real-time automated system for the recognition of human facial expressions, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 36, no. 1, pp. 96-105, Feb. 2006.
3. X. Li, S. Lin, S. Yan, and D. Xu, "Discriminant locally linear embedding with high-order tensor data, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 2, pp. 342-352, Apr. 2008.
4. Y. Yang, H. T. Shen, F. Nie, R. Ji, and X. Zhou, "Nonnegative spectral clustering with discriminative regularization. " in Proc. AAAI, 2011, pp. 555-560.
5. L. Liu, L. Shao, X. Zhen, and X. Li, "Learning discriminative key poses for action recognition, " IEEE Trans. Cybern., vol. 43, no. 6, pp. 1860-1870, Dec. 2013.
6. H. Zhang, J. Ho, Q. Wu, and Y. Ye, "Multidimensional latent semantic analysis using term spatial information, " IEEE Trans. Cybern., vol. 43, no. 6, pp. 1625-1640, Dec. 2013.
7. D. Tao, L. Jin, Z. Yang, and X. Li, "Rank preserving sparse learning for Kinect based scene classification, " IEEE Trans. Cybern., vol. 43, no. 5, pp. 1406-1417, Oct. 2013.
8. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York, NY, USA: Wiley-Interscience, 2001.
9. D. Lee and H. Seung, "Learning the parts of objects by nonnegative matrix factorization, " Nature, vol. 401, no. 6755, pp. 788-791, 1999.
10. S. Palmer, "Hierarchical structure in perceptual representation, " Cognitive Psychol., vol. 9, no. 4, pp. 441-474, 1977.
11. E. Wachsmuth, M. Oram, and D. Perrett, "Recognition of objects and their component parts: Responses of single units in the temporal cortex of the macaque, " Cereb. Cortex, vol. 4, no. 5, pp. 509-522, 1994.
12. W. Xu, X. Liu, and Y. Gong, "Document clustering based on nonnegative matrix factorization, " in Proc. 26th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2003, pp. 267-273.
13. S. Li, X. Hou, H. Zhang, and Q. Cheng, "Learning spatially localized, parts-based representation, " in Proc. Comput. Vis. Pattern Recognit., vol. 1. 2001, pp. I-207.
14. R. Zhi, M. Flierl, Q. Ruan, and W. B. Kleijn, "Graph-preserving sparse nonnegative matrix factorization with application to facial expression recognition, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 41, no. 1, pp. 38-52, Feb. 2011.
15. D. Cai, X. He, J. Han, and T. Huang, "Graph regularized nonnegative matrix factorization for data representation, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1548-1560, Aug. 2011.
16. Q. Gu and J. Zhou, "Local learning regularized nonnegative matrix factorization, " in Proc. IJCAI, 2009, pp. 1046-1051.
17. N. Guan, D. Tao, Z. Luo, and B. Yuan, "Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent, " IEEE Trans. Image Process., vol. 20, no. 7, pp. 2030-2048, Jul. 2011.
18. C. Ding, T. Li, and M. Jordan, "Convex and semi-nonnegative matrix factorizations, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 32, no. 1, pp. 45-55, Jan. 2010.
19. R. Sandler and M. Lindenbaum, "Nonnegative matrix factorization with earth mover's distance metric, " in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2009, pp. 1873-1880.
20. H. Liu, Z. Yang, Z. Wu, and X. Li, "A-optimal non-negative projection for image representation, " in Proc. Comput. Vis. Pattern Recognit., 2012, pp. 1592-1599.
21. C. Ding, X. He, and H. Simon, "On the equivalence of nonnegative matrix factorization and spectral clustering, " in Proc. SIAM Data Mining Conf., 2005, pp. 606-610.
22. P. Hoyer, "Non-negative matrix factorization with sparseness constraints, " J. Mach. Learn. Res., vol. 5, pp. 1457-1469, Nov. 2004.
23. A. Hyvärinen, P. Hoyer, and M. Inki, "Topographic independent component analysis, " Neural Comput., vol. 13, no. 7, pp. 1527-1558, 2001.
24. Q. Le, A. Karpenko, J. Ngiam, and A. Ng, "ICA with reconstruction cost for efficient overcomplete feature learning, " in Proc. Adv. Neural Inform. Process. Syst., 2011, pp. 1017-1025.
25. Q. Le, R. Monga, M. Devin, K. Chen, G. Corrado, J. Dean, et al., "Building high-level features using large-scale unsupervised learning, " in Proc. Int. Conf. Mach. Learn., 2012, pp. 1-8.
26. D. Seung and L. Lee, "Algorithms for non-negative matrix factorization, " in Proc. Adv. Neural Inf. Process. Syst., vol. 13. 2001, pp. 556-562.
27. I. Goodfellow, Q. Le, A. Saxe, H. Lee, and A. Ng, "Measuring invariances in deep networks, " in Proc. Adv. Neural Inf. Process. Syst., Dec. 2009, pp. 646-654.
28. A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EM algorithm, " J. Roy. Stat. Soc. B, vol. 39, no. 1, pp. 1-38, 1977.
29. B. A. Olshausen, and D. J. Field, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images, " Nature, vol. 381, no. 6583, pp. 607-609, 1996.
30. L. Fei-Fei, R. Fergus, and P. Perona, "Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories, " Comput. Vis. Image Und., vol. 106, no. 1, pp. 59-70, 2007.
31. D. G. Lowe, "Object recognition from local scale-invariant features, " in Proc. Int. Conf. Comput. Vis., vol. 2. 1999, pp. 1150-1157.
32. J. Sivic and A. Zisserman, "Video google: A text retrieval approach to object matching in videos, " in Proc. Int. Conf. Comput. Vis., 2003, pp. 1470-1477.
33. L. Lovasz and M. Plummer, "Matching theory, " Akademiai Kiado, 1986.
34. J. Dem? s ar, "Statistical comparisons of classifiers over multiple data sets, " J. Mach. Learn. Res., vol. 7, pp. 1-30, Jan. 2006.