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Oral
Partially View-aligned Clustering
Zhenyu Huang · Peng Hu · Joey Tianyi Zhou · Jiancheng Lv · Xi Peng

Tue Dec 08 06:30 AM -- 06:45 AM (PST) @ Orals & Spotlights: Clustering/Ranking
in Orals & Spotlights Track 05: Clustering/Ranking »
In this paper, we study one challenging issue in multi-view data clustering. To be specific, for two data matrices $\mathbf{X}^{(1)}$ and $\mathbf{X}^{(2)}$ corresponding to two views, we do not assume that $\mathbf{X}^{(1)}$ and $\mathbf{X}^{(2)}$ are fully aligned in row-wise. Instead, we assume that only a small portion of the matrices has established the correspondence in advance. Such a partially view-aligned problem (PVP) could lead to the intensive labor of capturing or establishing the aligned multi-view data, which has less been touched so far to the best of our knowledge. To solve this practical and challenging problem, we propose a novel multi-view clustering method termed partially view-aligned clustering (PVC). To be specific, PVC proposes to use a differentiable surrogate of the non-differentiable Hungarian algorithm and recasts it as a pluggable module. As a result, the category-level correspondence of the unaligned data could be established in a latent space learned by a neural network, while learning a common space across different views using the ``aligned'' data. Extensive experimental results show promising results of our method in clustering partially view-aligned data.

Author Information

Zhenyu Huang (Sichuan University)
Peng Hu (Sichuan University)
Joey Tianyi Zhou (IHPC, A*STAR)
Jiancheng Lv (Machine Intelligence Laboratory College of Computer Science, Sichuan University)
Xi Peng (College of Computer Science, Sichuan University)

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