Soft-label guided non-negative matrix factorization for unsupervised feature selection

Abstract

Unsupervised feature selection (UFS) aims to select the most representative features from the original data, which can efficiently reduce the influence of redundancy, outliers and noises. Over the past decades, various UFS algorithms have been proposed. However, these methods often do not consider the necessity of sparsity or ignore the fuzziness of the data. To tackle these shortcomings, in this paper, a novel soft-label guided non-negative matrix factorization (SLNMF) method is proposed. Specifically, both the convex NMF and ℓ2,1−norm regularization are introduced to ensure the sparsity of the feature selection matrix. Furthermore, the soft-label matrix based on local distance is used to supervise the feature selection, and a linear regression is developed to find the correlation between the low-dimensional representation and the soft-label space. Finally, extensive experiments on several benchmark datasets are conducted. The results show that the proposed method is advanced over several state-of-the-art UFS methods.