Image feature selection based on orthogonal [formula omitted] norms

Abstract

This study presents a feature selection method based on orthogonal ℓ2,0-norms to reduce dimensions, especially for images, where correlated and redundant information is frequently present by nature. Recent ℓ2,0-norm methods have shown a way of discovering sparsity, but redundant features could still be selected in the process. In light of such, this study considers imposing an orthogonal constraint on sparsity, further limiting ℓ2,0 norms. To such an end, projection onto Stiefel manifolds is computed to satisfy the orthogonal constraint while ℓ2,0-norm regularization is computed via ℓp-box zero-one programming. Experiments on open datasets were carried out to evaluate the proposed method and different models, including ℓ1-, ℓ2,1-, and ℓ2,0-norm approaches. The experimental results showed that the mean accuracy and F1 scores of the proposed method were higher than those of the ℓ2,0-norm method without orthogonal constraints and those of the other baselines, subsequently proving the effectiveness of the proposed idea.