Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Signal Processing - Special issue: Genomic signal processing
A robust minimax approach to classification
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Clustering based large margin classification: a scalable approach using SOCP formulation
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminative semi-supervised feature selection via manifold regularization
IEEE Transactions on Neural Networks
Learning algorithms for link prediction based on chance constraints
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Linear penalization support vector machines for feature selection
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Pattern Recognition Letters
A unified classification model based on robust optimization
Neural Computation
Alternative second-order cone programming formulations for support vector classification
Information Sciences: an International Journal
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This paper addresses the issue of feature selection for linear classifiers given the moments of the class conditional densities. The problem is posed as finding a minimal set of features such that the resulting classifier has a low misclassification error. Using a bound on the misclassification error involving the mean and covariance of class conditional densities and minimizing an L1 norm as an approximate criterion for feature selection, a second order programming formulation is derived. To handle errors in estimation of mean and covariances, a tractable robust formulation is also discussed. In a slightly different setting the Fisher discriminant is derived. Feature selection for Fisher discriminant is also discussed. Experimental results on synthetic data sets and on real life microarray data show that the proposed formulations are competitive with the state of the art linear programming formulation.