ADAPTIVE FEATURE WEIGHT LEARNING FOR ROBUST CLUSTERING PROBLEM WITH SPARSE CONSTRAINT

Clustering task has been greatly developed in recent years like partition-based and graph-based methods. However, in terms of improving robustness, most existing algorithms only focus on noise and outliers between data, while ignoring the noise in feature space. To deal with this situation, we propose a novel weight learning mechanism to adaptively reweight each feature in the data. Combining with the clustering task, we further propose a robust fuzzy K-Means model based on the auto-weighted feature learning, which can effectively reduce the proportion of noisy features. Besides, a regularization term is introduced into our model to make the sample-to-clusters memberships of each sample have suitable sparsity. Specifically, we design an effective strategy to determine the value of the regularization parameter. The experimental results on both synthetic and real-world datasets demonstrate that our model has better performance than other classical algorithms.