Fr'echet regression for multi-label feature selection with implicit regularization

2024-12-24

AI Summary

研究通过提出一种基于Fréchet回归的新方法，解决了多标签分类中关键特征选择的问题，该方法简化了问题处理，增强了线性回归能力，准确捕捉预测与结果间的复杂关系，且模型更简洁。

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Multi-modal Vision
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Fr'echet regression extends linear regression to model complex responses in metric spaces, making it particularly relevant for multi-label regression, where each instance can have multiple associated labels. However, variable selection within this framework remains underexplored. In this paper, we pro pose a novel variable selection method that employs implicit regularization instead of traditional explicit regularization approaches, which can introduce bias. Our method effectively captures nonlinear interactions between predic tors and responses while promoting model sparsity. We provide theoretical results demonstrating selection consistency and illustrate the performance of our approach through numerical examples

Problem

• Multi-label Classification
• Feature Selection
• Model Simplification

Innovation

• Frechet Regression
• Multi-label Classification
• Feature Selection