Feature Selection Based on Orthogonal Constraints and Polygon Area

arXiv:2402.16026v1 Announce Type: new
Abstract: 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 …

abstract arxiv constraints cs.lg dependencies dimensionality feature features feature selection importance labels polygon recognition type