Publication Date: 5/4/2019
Event: SIAM International Conference on Data Mining (SDM 2019)
Reference: pp. 414-422, 2019
Authors: Dongkuan Xu, NEC Laboratories America, Inc.; Pennsylvania State University; Wei Cheng, NEC Laboratories America, Inc.; Bo Zong, NEC Laboratories America, Inc.; Jingchao Ni, NEC Laboratories America, Inc.; Dongjin Song, NEC Laboratories America, Inc.; Wenchao Yu, NEC Laboratories America, Inc.; Yuncong Chen , NEC Laboratories America, Inc.; Haifeng Chen, NEC Laboratories America, Inc.; Xiang Zhang, Pennsylvania State University
Abstract: Co-clustering partitions instances and features simultaneously by leveraging the duality between them and it often yields impressive performance improvement over traditional clustering algorithms. The recent development in learning deep representations has demonstrated the advantage in extracting effective features. However, the research on leveraging deep learning frameworks for co-clustering is limited for two reasons: 1) current deep clustering approaches usually decouple feature learning and cluster assignment as two separate steps, which cannot yield the task-specific feature representation; 2) existing deep clustering approaches cannot learn representations for instances and features simultaneously. In this paper, we propose a deep learning model for co-clustering called DeepCC. DeepCC utilizes the deep autoencoder for dimension reduction, and employs a variant of Gaussian Mixture Model (GMM) to infer the cluster assignments. A mutual information loss is proposed to bridge the training of instances and features. DeepCC jointly optimizes the parameters of the deep autoencoder and the mixture model in an end-to-end fashion on both the instance and the feature spaces, which can help the deep autoencoder escape from local optima and the mixture model circumvent the Expectation-Maximization (EM) algorithm. To the best of our knowledge, DeepCC is the first deep learning model for co-clustering. Experimental results on various dataseis demonstrate the effectiveness of DeepCC.
Publication Link: https://epubs.siam.org/doi/10.1137/1.9781611975673.47