An NEPv Approach for Feature Selection via Orthogonal OCCA with the (2,1)-norm Regularization

Li Wang; Lei-Hong Zhang; Ren-Cang Li

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arXiv · 2502.18633

An NEPv Approach for Feature Selection via Orthogonal OCCA with the (2,1)-norm Regularization

Li Wang·Lei-Hong Zhang·Ren-Cang Li

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Abstract

A novel feature selection model via orthogonal canonical correlation analysis with the $(2,1)$-norm regularization is proposed, and the model is solved by a practical NEPv approach (nonlinear eigenvalue problem with eigenvector dependency), yielding a feature selection method named OCCA-FS. It is proved that OCCA-FS always produces a sequence of approximations with monotonic objective values and is globally convergent. Extensive numerical experiments are performed to compare OCCA-FS against existing feature selection methods. The numerical results demonstrate that OCCA-FS produces superior classification performance and often comes out on the top among all feature selection methods in comparison.

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Li Wang, Lei-Hong Zhang, Ren-Cang Li. 2025-02-25. An NEPv Approach for Feature Selection via Orthogonal OCCA with the (2,1)-norm Regularization. https://arxiv.org/abs/2502.18633

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