Contrastive Multi-view Hyperbolic Hierarchical Clustering
This addresses the problem of understanding hierarchical structures in multi-view data for applications like bioinformatics or social networks, but it is incremental as it builds on existing hierarchical clustering and multi-view methods.
The paper tackles multi-view hierarchical clustering by proposing a neural network model called CMHHC, which aligns multi-view representations, learns similarities, and embeds them in hyperbolic space to decode a clustering tree, achieving effective results on five real-world datasets.
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.