Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering
This addresses the problem of reducing data complexity and improving readability for machine learning practitioners, particularly in scenarios with large unlabeled datasets, representing an incremental advancement in unsupervised feature selection.
The paper tackles unsupervised feature selection by proposing a method that uses adaptive similarity learning and subspace clustering to preserve sample similarities and capture discriminative information, demonstrating effectiveness on benchmark datasets compared to state-of-the-art methods.
Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms. In recent years, a variety of efforts are investigated on feature selection problems based on unsupervised viewpoint due to the laborious labeling task on large datasets. In this paper, we propose a novel approach on unsupervised feature selection initiated from the subspace clustering to preserve the similarities by representation learning of low dimensional subspaces among the samples. A self-expressive model is employed to implicitly learn the cluster similarities in an adaptive manner. The proposed method not only maintains the sample similarities through subspace clustering, but it also captures the discriminative information based on a regularized regression model. In line with the convergence analysis of the proposed method, the experimental results on benchmark datasets demonstrate the effectiveness of our approach as compared with the state of the art methods.