Deep Fusion Clustering Network
Keywords:Multimodal Learning, Clustering
AbstractDeep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structure information for clustering performance enhancement. However, we observe that existing literature 1) lacks a dynamic fusion mechanism to selectively integrate and refine the information of graph structure and node attributes for consensus representation learning; 2) fails to extract information from both sides for robust target distribution (i.e., “groundtruth” soft labels) generation. To tackle the above issues, we propose a Deep Fusion Clustering Network (DFCN). Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning. Also, a reliable target distribution generation measure and a triplet self-supervision strategy, which facilitate cross-modality information exploitation, are designed for network training. Extensive experiments on six benchmark datasets have demonstrated that the proposed DFCN consistently outperforms the state-of-the-art deep clustering methods.
How to Cite
Tu, W., Zhou, S., Liu, X., Guo, X., Cai, Z., Zhu, E., & Cheng, J. (2021). Deep Fusion Clustering Network. Proceedings of the AAAI Conference on Artificial Intelligence, 35(11), 9978-9987. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17198
AAAI Technical Track on Machine Learning IV