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Much more attention has been paid to unsupervised feature selection nowadaysdue to the emergence of massive unlabeled data. The distribution of samples andthe latent effect of training a learning method using samples in more effectiveorder need to be considered so as to improve the robustness of the method.Self-paced learning is an effective method considering the training order ofsamples. In this study, an unsupervised feature selection is proposed byintegrating the framework of self-paced learning and subspace learning.Moreover, the local manifold structure is preserved and the redundancy offeatures is constrained by two regularization terms. $L_{2,1/2}$-norm isapplied to the projection matrix, which aims to retain discriminative featuresand further alleviate the effect of noise in the data. Then, an iterativemethod is presented to solve the optimization problem. The convergence of themethod is proved theoretically and experimentally. The proposed method iscompared with other state of the art algorithms on nine real-world datasets.The experimental results show that the proposed method can improve theperformance of clustering methods and outperform other compared algorithms.

Unsupervised feature selection via self-paced learning and low-redundant regularization