Unsupervised feature selection via self-paced learning and low-redundant regularization
This work addresses the problem of selecting features from unlabeled data for researchers and practitioners in machine learning, but it is incremental as it builds on existing self-paced learning and regularization techniques.
The paper tackles unsupervised feature selection by integrating self-paced learning and subspace learning with regularization to preserve local manifold structure and reduce feature redundancy, resulting in improved clustering performance that outperforms state-of-the-art algorithms on nine real-world datasets.
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.