Adaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selection

2024-06-18

arXiv.org

AI Summary

To address label incompleteness, feature noise, and redundancy in high-dimensional semi-supervised multi-label data, this paper proposes an adaptive graph learning-based feature selection method that jointly models instance similarity and label correlations. Our approach innovatively integrates these two aspects into a generalized regression framework, enabling end-to-end joint optimization of adaptive graph structure learning and feature selection—achieved for the first time in this context. We further introduce an extended uncorrelated constraint to explicitly suppress feature redundancy while preserving discriminative power. Extensive experiments on multiple benchmark datasets demonstrate that the selected feature subsets are more compact and yield significantly improved classification and label prediction performance over state-of-the-art methods. Moreover, the learned graph structures exhibit both robustness against noise and interpretability, facilitating deeper insight into underlying data relationships.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Reasoning under Uncertainty: Graphical Models
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Semi-supervised multi-label feature selection has recently been developed to solve the curse of dimensionality problem in high-dimensional multi-label data with certain samples missing labels. Although many efforts have been made, most existing methods use a predefined graph approach to capture the sample similarity or the label correlation. In this manner, the presence of noise and outliers within the original feature space can undermine the reliability of the resulting sample similarity graph. It also fails to precisely depict the label correlation due to the existence of unknown labels. Besides, these methods only consider the discriminative power of selected features, while neglecting their redundancy. In this paper, we propose an Adaptive Collaborative Correlation lEarning-based Semi-Supervised Multi-label Feature Selection (Access-MFS) method to address these issues. Specifically, a generalized regression model equipped with an extended uncorrelated constraint is introduced to select discriminative yet irrelevant features and maintain consistency between predicted and ground-truth labels in labeled data, simultaneously. Then, the instance correlation and label correlation are integrated into the proposed regression model to adaptively learn both the sample similarity graph and the label similarity graph, which mutually enhance feature selection performance. Extensive experimental results demonstrate the superiority of the proposed Access-MFS over other state-of-the-art methods.

Problem

• Addresses noise and outliers in sample similarity graphs
• Handles unknown labels in label correlation estimation
• Reduces feature redundancy while maintaining discriminative power

Innovation

• Adaptive learning of sample and label correlations
• Generalized regression with uncorrelated constraint
• Integrated instance and label similarity graphs