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

TL;DR

This paper introduces Access-MFS, a semi-supervised multi-label feature selection method that adaptively learns sample and label correlations to improve feature relevance and reduce noise impact.

Contribution

It proposes a novel adaptive collaborative correlation learning framework that jointly optimizes sample and label similarity graphs for better feature selection in high-dimensional data.

Findings

Outperforms state-of-the-art methods in experiments

Effectively handles noise and outliers in data

Improves feature relevance and reduces redundancy

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.