Unsupervised feature selection via local structure learning and sparse learning

Feature self-representation has become the backbone of unsupervised feature selection, since it is almost insensitive to noise data. However, feature selection methods based on feature self-representation have the following drawbacks: 1) The self-representation coefficient matrix is fixed and can not be fine-tuned according to the structure of data. 2) they do not consider the manifold structure of data, thus unable to further increase the performance of feature selection. To solve the above problems, this paper proposes an unsupervised feature selection algorithm that combines feature self-representation and manifold learning. Specifically, we first utilize feature self-representation to construct the model. After that, the self-representation coefficient matrix is dynamically adjusted to the optimal state based on the similarity matrix. Then, we use low-rank representation to explore the global manifold structure of the data. Finally, we combine sparse learning with feature selection. The experimental results on twelve datasets show that the proposed method outperforms all the competing methods.