Complemented subspace-based weighted collaborative representation model for imbalanced learning

doi:10.1016/j.asoc.2024.111319

Complemented subspace-based weighted collaborative representation model for imbalanced learning

2024-03-01

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AI

陈晨 (C. L. Philip Chen)

DOI：10.1016/j.asoc.2024.111319

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Abstract

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Collaborative representation -based classifiers (CRCs) have demonstrated remarkable classification performance in various pattern recognition fields. However, their success heavily relies on a balanced class distribution. More radically, significantly skewed class distributions may severely affect the effectiveness of the CRCs. To address this limitation, in this study, a complemented subspace-based regularization technique is integrated into the CRC framework for tackling imbalanced classification tasks. To enhance recognition accuracy for minority classes, a class weight learning algorithm is presented, in which the weight of each class is adaptively obtained according to the prior distribution information of the original data. Furthermore, the proposed model provides an efficient closed-form solution, ensuring computational efficiency comparable to CRCs. Extensive experiments on diverse imbalanced datasets are carried out to substantiate the superiority of our method over numerous advanced imbalanced learning algorithms.

Keywords:

Imbalanced learning

Collaborative representation

Complemented subspace

Class weight

Regularization

Complemented subspace-based weighted collaborative representation model for imbalanced learning

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