Deep embedded clustering with adversarial distribution adaptation

Abstract

Clustering methods based on deep neural networks have been extensively studied and applied in data mining. Most existing unsupervised deep embedded clustering methods jointly learn feature representations and cluster assignments with deep neural networks. However, to the best of our knowledge, most of them ignore the relevance between representation learning and clustering, so that fail to couple feature representation learning and cluster assignments effectively, which leads to non-representative features for clustering and this in turn hurts clustering performance. In this paper, we propose Adversarial Deep Embedded Clustering (ADEC), a novel unsupervised clustering method based on adversarial auto-encoder (AAE) and k-means clustering method. Specifically, ADEC matches distribution of feature representations with the given prior distribution to preserve the data structure with AAE, and k-means based on distribution distance metrics is conducted for clustering with these feature representations, simultaneously. ADEC optimizes a clustering objective iteratively with backpropagation algorithm in learning AAE from data space to feature space. Extensive experiments have demonstrated the effectiveness of our proposed method on images clustering compared to several strong baseline methods.