Partially View-aligned Clustering

Partially View-aligned Clustering
Title:
Partially View-aligned Clustering
Other Titles:
Advances in Neural Information Processing Systems
DOI:
Publication Date:
01 December 2020
Citation:
Huang Z, Hu P, Zhou J T, et al. Partially View-aligned Clustering[J]. Advances in Neural Information Processing Systems, 2020, 33.
Abstract:
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.
License type:
Funding Info:
This work was supported in part by NFSC under Grant U19A2081, 61625204, and 61836006; in part by the Fundamental Research Funds for the Central Universities under Grant YJ201949; in part by the Fund of Sichuan University Tomorrow Advancing Life; and in part by A*STAR AME Programmatic under Grant A18A1b0045.
Description:
ISBN:

Files uploaded:
File Size Format Action
There are no attached files.