From Bi-Level Sparse Clustering to Deep Clustering
Zhangyang Wang Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States
Many clustering methods highly depend on extracted features. We propose a joint optimization framework in terms of both feature extraction and discriminative clustering. We utilize graph regularized sparse codes as the features, and formulate sparse coding as the constraint for clustering. Two cost functions are developed based on entropy-minimization and maximum-margin clustering principles, respectively. They are considered as the objectives to be minimized. Solving such a bi-level optimization mutually reinforces both sparse coding and clustering ...
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