Structured sparse linear graph embedding

Neural Networks

Volumn 27, Issue , 2012, Pages 38-44

  Wang, Haixian ^a  

^a Southeast University   (China)

Author keywords

Face recognition; Linear graph embedding; Overlapping grouped lasso penalty; Structured sparsity

Indexed keywords

CLOSED FORM; FACE IMAGES; HIGH-ORDER; LINEAR GRAPH; OPTIMIZATION PROBLEMS; OVERLAPPING GROUPED LASSO PENALTY; PROCRUSTES; REGRESSION COEFFICIENT; STRUCTURE INFORMATION; STRUCTURED SPARSITY; SUBSPACE LEARNING; UNIFIED FRAMEWORK;

ARTIFICIAL INTELLIGENCE; COGNITIVE SYSTEMS;

FACE RECOGNITION;

ARTICLE; AUTOMATED PATTERN RECOGNITION; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; FACIAL EXPRESSION; IMAGE ANALYSIS; LEARNING ALGORITHM; LINEAR GRAPH EMBEDDING; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MATHEMATICAL PARAMETERS; PRIORITY JOURNAL; STRUCTURED SPARSE LINEAR GRAPH EMBEDDING; VISION;

ARTIFICIAL INTELLIGENCE; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED;

EID: 84856456364     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2011.11.003     Document Type: Article

References (29)

1. Baraniuk R.G. Compressive sensing. IEEE Signal Processing Magazine 2007, 24(4):118-120.
2. Baraniuk R.G., Cevher V., Duarte M.F., Hegde C. Model-based compressive sensing. IEEE Transactions on Information Theory 2010, 56(4):1982-2001.
3. Belkin M., Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 2003, 15:1373-1396.
4. Cai, D., He, X., & Han, J. (2007a). Spectral regression: a unified approach for sparse subspace learning. In Proceedings of the seventh IEEE international conference on data mining (pp. 73-82).
5. Cai, D., He, X., & Han, J. (2007b). Isometric projection. In Proceedings of the artificial intelligence.
6. Cai, D., He, X., & Han, J. (2008). Sparse projections over graph. In Proceedings of the artificial intelligence.
7. Duda R.O., Hart P.E., Stork D.G. Pattern classification 2001, Wiley, New York.
8. Gower J.C., Dijksterhuis G.B. Procrustes problems 2004, Oxford University Press, New York.
9. He, X., Cai, D., Yan, S., & Zhang, H.-J. (2005a). Neighborhood preserving embedding. In Proceedings of the tenth IEEE international conference on computer vision. Vol. 2 (pp. 1208-1213).
10. He X., Yan S., Hu Y., Niyogi P., Zhang H.-J. Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005, 27(3):328-340.
11. Huang, J., Zhang, T., & Metaxas, D. (2009). Learning with structured sparsity. In Proceedings of the 26th international conference on machine learning.
12. Jacob, L., Obozinski, G., & Vert, J.P. (2009). Group lasso with overlap and graph lasso. In Proceedings of the 26th international conference on machine learning.
13. Jain A.K., Duin R.P.W., Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22(1):4-37.
14. Jenatton, R., Audibert, J.-Y., & Bach, F. (2010a). Structured variable selection with sparsity-inducing norms. Technical report. arxiv:0904.3523v3.
15. Jenatton, R., Obozinski, G., & Bach, F. (2010b). Structured sparse principal component analysis. In International conference on artificial intelligence and statistics.
16. Lu B.-W., Pandolfo L. Quasi-objective nonlinear principal component analysis. Neural network 2011, 24(2):159-170.
17. Mhaskar H.N. A generalized diffusion frame for parsimonious representation of functions on data defined manifolds. Neural network 2011, 24(4):345-359.
18. Phillips P.J., Moon H., Rizvi S.A., Rauss P.J. The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22(10):1090-1104.
19. Qiao L., Chen S., Tan X. Sparsity preserving projections with applications to face recognition. Pattern Recognition 2010, 43:331-341.
20. Qiao Z., Zhou L., Huang J.Z. Sparse linear discriminant analysis with applications to high dimensional low sample size data. IAENG International Journal of Applied Mathematics 2009, 39(1):48-60.
21. Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290:2323-2326.
22. Shi, J., Ren, X., Dai, G., Wang, J., & Zhang, Z. (2011). A non-convex relaxation approach to sparse dictionary learning. In Proceedings of the IEEE international conference on computer vision (pp. 1809-1816).
23. Tenenbaum J.B., de Silva V., Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290:2319-2323.
24. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B 1996, 58:267-288.
25. Yan S., Xu D., Zhang B., Zhang H.-J., Yang Q., Lin S. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007, 29(1):40-51.
26. Yuan M., Lin Y. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B 2006, 68(1):49-67.
27. Zhou T., Tao D., Wu X. Manifold elastic net: a unified framework for sparse dimension reduction. Data Mining and Knowledge Discovery 2011, 22:340-371.
28. Zou H., Hastie T., Tibshirani R. Sparse principal component analysis. Journal of Computational and Graphical Statistics 2006, 15(2):265-286.
29. Zou H., Li R. One-step sparse estimates in nonconcave penalized likelihood models. Annals of Statistics 2008, 36(4):1509-1533.