A review of the current status and future directions of research on subspace clustering feature selection

Feature selection reduces the dimensionality of high-dimensional data by removing redundant or irrelevant features from the original features, thus reducing the negative impact of the "dimensionality curse." Subspace clustering feature selection methods focus on the structure and properties within the dataset, so they perform well in unsupervised feature selection work. In this paper, we sort out and classify the research on subspace clustering feature selection and propose several future research trends based on the current status of feature selection in subspace clustering.