Feature Selection Based on Orthogonal Constraints and Polygon Area

• URL: http://arxiv.org/abs/2402.16026v1
• Date: Sun, 25 Feb 2024 08:20:05 GMT
• Title: Feature Selection Based on Orthogonal Constraints and Polygon Area
• Authors: Zhenxing Zhang and Jun Ge and Zheng Wei and Chunjie Zhou and Yilei Wang
• Abstract summary: The goal of feature selection is to choose the optimal subset of features for a recognition task by evaluating the importance of each feature. This paper introduces a non-monotone linear search between dependencies enhancing features labels. Experimental results demonstrate that our approach not only effectively captures discriminative dependency but also surpasses traditional methods in reducing dimensions classification performance.
• Score: 10.587608254638667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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