Multi-label feature selection via similarity constraints with non-negative matrix factorization

Abstract

Feature selection plays a key role in preprocessing, effectively addressing the curse of dimensionality in multi-label learning. While current approaches commonly utilize feature or label similarity to construct the weight matrix, typically through manifold learning regularization, there has been a dearth of progress in developing similarity-constrained regularization terms for multi-label feature selection. To address this gap, this paper conducts a comprehensive investigation and proposes a novel similarity constraint leveraging non-negative matrix factorization techniques. Subsequently, a new algorithm is introduced termed Multi-label Feature Selection via Similarity Constraints with Non-negative Matrix Factorization (SCNMF). Initially, the Gaussian similarity matrix among features is computed and factorized into a weight matrix using non-negative matrix factorization. Subsequently, the Cosine distance matrix among labels is computed to constrain the weight matrix. Finally, an objective function is formulated based on the aforementioned constraint mechanisms and iteratively optimized. Extensive experiments conducted across more than 10 multi-label datasets demonstrate the superior performance of our proposed approach. The source code is accessible at the URL: https://github.com/BIGOatMNNU/SCNMF.