# One-Step Multi-View Clustering Based on Transition Probability

arXiv: https://arxiv.org/abs/2403.01460 · HTML: https://mlsanity.com/paper/2403.01460/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Builds on (most cited first, 4 of 4)

- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [Essential Tensor Learning for Multi-view Spectral Clustering](https://mlsanity.com/paper/1807.03602/) (2018)
- [Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction](https://mlsanity.com/paper/1512.01003/) (2015)
- [A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization](https://mlsanity.com/paper/1709.03979/) (2017)

## Abstract (arXiv)

The large-scale multi-view clustering algorithms, based on the anchor graph, have shown promising performance and efficiency and have been extensively explored in recent years. Despite their successes, current methods lack interpretability in the clustering process and do not sufficiently consider the complementary information across different views. To address these shortcomings, we introduce the One-Step Multi-View Clustering Based on Transition Probability (OSMVC-TP). This method adopts a probabilistic approach, which leverages the anchor graph, representing the transition probabilities from samples to anchor points. Our method directly learns the transition probabilities from anchor points to categories, and calculates the transition probabilities from samples to categories, thus obtaining soft label matrices for samples and anchor points, enhancing the interpretability of clustering. Furthermore, to maintain consistency in labels across different views, we apply a Schatten p-norm constraint on the tensor composed of the soft labels. This approach effectively harnesses the complementary information among the views. Extensive experiments have confirmed the effectiveness and robustness of OSMVC-TP.