Self Supervised Correlation-based Permutations for Multi-View Clustering
This addresses the need for efficient and generalizable multi-view clustering methods across domains like images and tables, though it is incremental in improving existing approaches.
The paper tackled the problem of multi-view clustering by proposing an end-to-end deep learning framework that generates fused representations using a novel permutation-based canonical correlation objective, achieving improved performance on ten benchmark datasets.
Combining data from different sources can improve data analysis tasks such as clustering. However, most of the current multi-view clustering methods are limited to specific domains or rely on a suboptimal and computationally intensive two-stage process of representation learning and clustering. We propose an end-to-end deep learning-based multi-view clustering framework for general data types (such as images and tables). Our approach involves generating meaningful fused representations using a novel permutation-based canonical correlation objective. We provide a theoretical analysis showing how the learned embeddings approximate those obtained by supervised linear discriminant analysis (LDA). Cluster assignments are learned by identifying consistent pseudo-labels across multiple views. Additionally, we establish a theoretical bound on the error caused by incorrect pseudo-labels in the unsupervised representations compared to LDA. Extensive experiments on ten multi-view clustering benchmark datasets provide empirical evidence for the effectiveness of the proposed model.