Direct Sparsity Optimization Based Feature Selection for Multi-Class Classification
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[1] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[2] E. Lander,et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[3] Bhaskar D. Rao,et al. Sparse solutions to linear inverse problems with multiple measurement vectors , 2005, IEEE Transactions on Signal Processing.
[4] Chris H. Q. Ding,et al. Efficient Algorithms for Selecting Features with Arbitrary Group Constraints via Group Lasso , 2013, 2013 IEEE 13th International Conference on Data Mining.
[5] Olvi L. Mangasarian,et al. Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization , 2006, J. Mach. Learn. Res..
[6] Michael I. Jordan,et al. Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates , 2003, NIPS.
[7] Feiping Nie,et al. Feature Selection via Joint Embedding Learning and Sparse Regression , 2011, IJCAI.
[8] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Hao Helen Zhang,et al. Support vector machines with adaptive Lq penalty , 2007, Comput. Stat. Data Anal..
[10] Michael I. Jordan,et al. Multi-task feature selection , 2006 .
[11] Feiping Nie,et al. Exclusive Feature Learning on Arbitrary Structures via \ell_{1, 2}-norm , 2014, NIPS.
[12] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[13] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[14] David D. Lewis,et al. Feature Selection and Feature Extraction for Text Categorization , 1992, HLT.
[15] Feiping Nie,et al. Feature Selection at the Discrete Limit , 2014, AAAI.
[16] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[17] Jieping Ye,et al. Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization , 2009, UAI.
[18] Aixia Guo,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2014 .
[19] Gérard Govaert,et al. An Efficient Approach to Sparse Linear Discriminant Analysis , 2012, ICML.
[20] Paul S. Bradley,et al. Feature Selection via Concave Minimization and Support Vector Machines , 1998, ICML.
[21] R. Chartrand,et al. Restricted isometry properties and nonconvex compressive sensing , 2007 .
[22] Gavin C. Cawley,et al. Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation , 2006, NIPS.
[23] Li Wang,et al. Hybrid huberized support vector machines for microarray classification , 2007, ICML '07.
[24] Lawrence Carin,et al. Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] WestonJason,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002 .
[26] Larry A. Rendell,et al. A Practical Approach to Feature Selection , 1992, ML.
[27] Han Liu,et al. Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery , 2009, ICML '09.
[28] Zi Huang,et al. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence ℓ2,1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning , 2022 .
[29] Feiping Nie,et al. Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization , 2010, NIPS.
[30] Feiping Nie,et al. Discriminative Least Squares Regression for Multiclass Classification and Feature Selection , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[31] Yufeng Liu,et al. Support vector machines with adaptive Lq penalty , 2007, Comput. Stat. Data Anal..
[32] Bhaskar D. Rao,et al. Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..