Auto-encoder Based Data Clustering
 |
| Framework of the proposed method |
People
Chunfeng Song
Feng Liu
Yongzhen Huang
Liang Wang
Tieniu Tan
Overview
Linear or non-linear data transformations are widely used processing techniques in clustering. Usually, they are beneficial to enhancing data representation. However, if data have a complex structure, these techniques would be unsatisfying for clustering. In this paper, based on the auto-encoder network, which can learn a highly non-linear mapping function, we propose a new clustering method. Via simultaneously considering data reconstruction and compactness, our method can obtain stable and effective clustering. Experiments on three databases show that the proposed clustering model achieves excellent performance in terms of both accuracy and normalized mutual information.
Paper
 |
Experimental Results
 |
| Performance comparison in three different spaces |
Acknowledgments
This work was jointly supported by National Basic Research Program of China (2012CB316300), National Natural Science Foundation of China (61175003, 61135002, 61203252), Tsinghua National Laboratory for Information Science and Technology Cross-discipline Foundation, and Hundred Talents Program of CAS.