Contrastive Multi-view Hyperbolic Hierarchical Clustering

• URL: http://arxiv.org/abs/2205.02618v1
• Date: Thu, 5 May 2022 12:56:55 GMT
• Title: Contrastive Multi-view Hyperbolic Hierarchical Clustering
• Authors: Fangfei Lin, Bing Bai, Kun Bai, Yazhou Ren, Peng Zhao and Zenglin Xu
• Abstract summary: We propose Contrastive Multi-view Hyperbolic Hierarchical Clustering (CMHHC) It consists of three components, i.e., multi-view alignment learning, aligned feature similarity learning, and continuous hyperbolic hierarchical clustering. Experimental results on five real-world datasets demonstrate the effectiveness of the proposed method.
• Score: 33.050054725595736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raises a less investigated problem, i.e., multi-view hierarchical clustering, to better understand the hierarchical structure of multi-view data. To this end, we propose a novel neural network-based model, namely Contrastive Multi-view Hyperbolic Hierarchical Clustering (CMHHC). It consists of three components, i.e., multi-view alignment learning, aligned feature similarity learning, and continuous hyperbolic hierarchical clustering. First, we align sample-level representations across multiple views in a contrastive way to capture the view-invariance information. Next, we utilize both the manifold and Euclidean similarities to improve the metric property. Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss. Finally, a binary clustering tree is decoded from optimized hyperbolic embeddings. Experimental results on five real-world datasets demonstrate the effectiveness of the proposed method and its components.

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