Multi-view Clustering based on Doubly Stochastic Graph

Nian Wang

doi:10.1016/j.sigpro.2025.110144

Multi-view Clustering based on Doubly Stochastic Graph

2025-06-17

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DOI：10.1016/j.sigpro.2025.110144

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Abstract

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• We introduce doubly stochastic graph learning to filter out the noise in a graph and propose a new model (MCDSG) for multi-view clustering. • We innovatively propose a simple yet highly effective approach to optimize the doubly stochastic condition. • We propose a pipeline to add noise to the key locations of face images and obtain a noisy face dataset termed noisedORL. • The experiments show our MCDSG is more robust to noised data and achieves SOTA clustering performance on benchmarks.

Keywords:

Multi-view Graph-based Clustering

Doubly Stochastic Graph learning

ALM based optimization

Multi-view Clustering based on Doubly Stochastic Graph

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