The goal of feature selection is to choose the optimal subset of features for
a recognition task by evaluating the importance of each feature, thereby
achieving effective dimensionality reduction. Currently, proposed feature
selection methods often overlook the discriminative dependencies between
features and labels. To address this problem, this paper introduces a novel
orthogonal regression model incorporating the area of a polygon. The model can
intuitively capture the discriminative dependencies between features and
labels. Additionally, this paper employs a hybrid non-monotone linear search
method to efficiently tackle the non-convex optimization challenge posed by
orthogonal constraints. Experimental results demonstrate that our approach not
only effectively captures discriminative dependency information but also
surpasses traditional methods in reducing feature dimensions and enhancing
classification performance.

本研究提出了一种新颖的正交回归模型，结合多边形的面积，以直观地捕捉特征与标签之间的区分性依赖，并采用混合非单调线性搜索方法来高效处理正交约束带来的非凸优化挑战，实验结果表明，这种方法不仅可以有效捕捉区分性依赖信息，而且在降低特征维度和提高分类性能方面优于传统方法。