Multi-Target Feature Selection with Adaptive Graph Learning and Target Correlations

Abstract

In this paper, we present a novel multi-target feature selection algorithm that incorporates adaptive graph learning and target correlations. Specifically, our proposed approach introduces the low-rank constraint on the regression matrix, allowing us to model both inter-target and input–output relationships within a unified framework. To preserve the similarity structure of the samples and mitigate the influence of noise and outliers, we learn a graph matrix that captures the induced sample similarity. Furthermore, we introduce a manifold regularizer to maintain the global target correlations, ensuring the preservation of the overall target relationship during subsequent learning processes. To solve the final objective function, we also propose an optimization algorithm. Through extensive experiments on eight real-world datasets, we demonstrate that our proposed method outperforms state-of-the-art multi-target feature selection techniques.