Deep Siamese Generative Adversarial Clustering

Deep clustering has attracted considerable attention owing to the inspiring performance in mining patterns of data. However, previous deep clustering methods cannot capture the category-invariant structures of data in mining patterns. Moreover, they cannot take into consideration the consistencies of data manifolds in learning clustering dependence between categories and instances. To address those challenges, deep Siamese generative adversarial clustering is proposed, which can capture the category-invariant structures and consistent manifolds of data in pattern mining. Specifically, the generative clustering is defined as the generation process of pairwise data within an expectation-maximization scheme, which explores the link information of pairwise data to facilitate consistent structures in clusters and different structures between clusters in fitting data distributions. Furthermore, a deep Siamese generative adversarial clustering network is proposed to implement the definition within a symmetrical architecture that contains generator, discriminator, and cluster networks, which can capture the category-invariant structures and consistencies of data manifolds in clustering. Afterwards, an adversarial clustering loss is defined based on the adversarial game of data distribution and the binary classification task of cluster structures to train the network parameters, which can facilitate the intra-cluster compactness and inter-cluster separation in partitioning data. Finally, extensive experiments are conducted on six benchmark datasets and the results illustrate the state-of-the-art performance of the proposed method in comparison with sixteen cutting-edge methods.