Self-weighted subspace clustering with adaptive neighbors

Subspace clustering has attracted increasing attention in recent years owing to its ability to process high-dimensional data effectively. However, existing subspace clustering methods often assume that different features are equally important, and on this basis, a similarity matrix is constructed to generate the clustering structure. However, this practice may significantly affect the clustering performance in cases where the importance of different features significantly differs or where many noisy features exist in the original data. To address these challenges, we propose a novel self-weighted subspace clustering method with adaptive neighbors (SWSCAN). A feature weighting scheme is introduced to assign appropriate weights to different features. Then, we use the self-expressive property and adaptive neighbors approach to capture both the global and local structures within the weighted data space. Moreover, we employ the alternating direction method of multipliers (ADMM) to effectively solve the optimization problem of SWSCAN. Empirical results on both synthetic and practical datasets validate that our proposed method outperforms other comparative clustering techniques and can learn appropriate weights for features.