Feature Selection Based on Orthogonal Constraints and Polygon Area
This work addresses feature selection for recognition tasks, offering a novel method to handle discriminative dependencies, though it appears incremental in the context of existing feature selection techniques.
The paper tackled the problem of feature selection by introducing an orthogonal regression model with polygon area to capture discriminative dependencies between features and labels, resulting in improved dimensionality reduction and classification performance compared to traditional methods.
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.