A Joint Optimization Framework of Sparse Coding and Discriminative Clustering
Abstract
Many clustering methods highly depend on extracted features. In this paper, 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, as the objectives to be minimized. Solving such a bi-level optimization mutually reinforces both sparse coding and clustering steps. Experiments on several benchmark datasets verify remarkable performance improvements led by the proposed joint optimization.
Cite
Text
Wang et al. "A Joint Optimization Framework of Sparse Coding and Discriminative Clustering." International Joint Conference on Artificial Intelligence, 2015.Markdown
[Wang et al. "A Joint Optimization Framework of Sparse Coding and Discriminative Clustering." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/wang2015ijcai-joint/)BibTeX
@inproceedings{wang2015ijcai-joint,
title = {{A Joint Optimization Framework of Sparse Coding and Discriminative Clustering}},
author = {Wang, Zhangyang and Yang, Yingzhen and Chang, Shiyu and Li, Jinyan and Fong, Simon and Huang, Thomas S.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2015},
pages = {3932-3938},
url = {https://mlanthology.org/ijcai/2015/wang2015ijcai-joint/}
}