From Lasso regression to Feature Vector Machine

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages 779-786

  Li, Fan ^a   Yang, Yiming ^a   Xing, Eric P ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE VECTORS; KERNEL MACHINE; NON-LINEAR MODEL; NONCONVEX OPTIMIZATION; NONLINEAR FEATURES; REDUNDANT FEATURES; SCALING KERNEL; SIMULATED DATA; SPARSE SOLUTIONS;

CONVEX OPTIMIZATION; MATHEMATICAL MODELS;

REGRESSION ANALYSIS;

EID: 67649142447     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

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3. Krishnapuram, B. et al. Joint classifier and feature optimization for cancer diagnosis using gene expression data. The Seventh Annual International Conference on Research in Computational Molecular Biology (RECOMB) 2003, ACM press, April 2003
4. Ng, A. Feature selection, L1 vs L2 regularization, and rotational invariance. ICML 2004
5. Perkins, S., Lacker, K. & Theiler, J. Grafting: Fast,Incremental Feature Selection by gradient descent in function space JMLR 2003 1333-1356
6. Roth, V. The Generalized LASSO. IEEE Transactions on Neural Networks (2004), Vol. 15, NO. 1.
7. Tibshirani, R. Optimal Reinsertion:Regression shrinkage and selection via the lasso. J.R.Statist. Soc. B(1996), 58,No.1, 267-288
8. Cover, TM. and Thomas, JA. Elements in Information Theory. New York: John Wiley & Sons Inc (1991).
9. Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T. and Vapnik V. Feature Selection for SVMs NIPS 13, 2000