Deep Distribution-preserving Incomplete Clustering with Optimal Transport

TL;DR

This paper introduces DDIC-OT, a deep clustering method that effectively handles incomplete high-dimensional data by using optimal transport for distribution measurement and integrating clustering with sample imputation.

Contribution

The paper proposes a novel deep incomplete clustering framework that combines distribution-preserving optimal transport with clustering loss, improving robustness against missing data.

Findings

Achieves superior clustering performance over state-of-the-art methods.

Demonstrates robustness across different missing data ratios.

Improves sample utilization through distribution-based evaluation.

Abstract

Clustering is a fundamental task in the computer vision and machine learning community. Although various methods have been proposed, the performance of existing approaches drops dramatically when handling incomplete high-dimensional data (which is common in real world applications). To solve the problem, we propose a novel deep incomplete clustering method, named Deep Distribution-preserving Incomplete Clustering with Optimal Transport (DDIC-OT). To avoid insufficient sample utilization in existing methods limited by few fully-observed samples, we propose to measure distribution distance with the optimal transport for reconstruction evaluation instead of traditional pixel-wise loss function. Moreover, the clustering loss of the latent feature is introduced to regularize the embedding with more discrimination capability. As a consequence, the network becomes more robust against missing features and the unified framework which combines clustering and sample imputation enables the two procedures to negotiate to better serve for each other. Extensive experiments demonstrate that the proposed network achieves superior and stable clustering performance improvement against existing state-of-the-art incomplete clustering methods over different missing ratios.