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