  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.
