Abstract:
This paper considers the linear-discriminant analysis (LDA) problem in the undersampled situation, in which the number of features is very large and the number of observa...Show MoreMetadata
Abstract:
This paper considers the linear-discriminant analysis (LDA) problem in the undersampled situation, in which the number of features is very large and the number of observations is limited. Sparsity is often incorporated in the solution of LDA to make a well interpretation of the results. However, most of the existing sparse LDA algorithms pursue sparsity by means of the ℓ1-norm. In this paper, we give elaborate analysis for nonconvex penalties, including the ℓ0-based and the sorted ℓ1-based LDA methods. The latter one can be regarded as a bridge between the ℓ0 and ℓ1 penalties. These nonconvex penalty-based LDA algorithms are evaluated on the gene expression array and face database, showing high classification accuracy on realworld problems.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 29, Issue: 10, October 2018)