Unsupervised Feature Selection via Feature-Grouping and Orthogonal Constraint
Aihong Yuan, Jiahao Huang, Wei Chen, Wenjie Zhang, Naidan Zhang, Mengbo You
Abstract
In the fields of machine learning and data mining, unsupervised feature selection plays an important role in processing large amounts of high-dimensional unlabeled data. This paper proposes an original and novel unsupervised feature selection based on feature grouping and orthogonal constraints. We consider the domain relationship in the original data and reconstruct the similarity matrix based on the correlation between the features. We use a generalized incoherent regression model based on orthogonal constraints. Furthermore, a graph regularization term with local structure preservation constraints is added to ensure that the feature subset does not lose local structural features in the original data space. Besides, an iterative algorithm is proposed to solve the optimization problem by iteratively updating the global similarity matrix, and constructing weight matrix, pseudo-label matrix and transformation matrix. Through experiments on 6 benchmark datasets, the clustering performance of the proposed method outperforms state-of-the-art unsupervised feature selection methods. The source code is available at: https://github.com/misteru/FGOC.