Self Supervised Correlation-based Permutations for Multi-View Clustering
Ran Eisenberg Jonathan Svirsky Ofir Lindenbaum
Fusing information from different modalities can enhance data analysis tasks,
including clustering. However, existing multi-view clustering (MVC) solutions
are limited to specific domains or rely on a suboptimal and computationally
demanding two-stage procedure of representation and clustering. We propose an
end-to-end deep learning-based MVC framework for general data (image, tabular,
etc.). Our approach involves learning meaningful fused data representations
with a novel permutation-based canonical correlation objective. Concurrently,
we learn cluster assignments by identifying consistent pseudo-labels across
multiple views. We demonstrate the effectiveness of our model using ten MVC
benchmark datasets. Theoretically, we show that our model approximates the
supervised linear discrimination analysis (LDA) representation. Additionally,
we provide an error bound induced by false-pseudo label annotations.