# TPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clustering

arXiv: https://arxiv.org/abs/2412.18847 · HTML: https://mlsanity.com/paper/2412.18847/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 25 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)
- [Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm](https://mlsanity.com/paper/1804.03728/) (2018)
- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction](https://mlsanity.com/paper/1808.00758/) (2018)
- [A Survey on Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.08040/) (2022)
- [Multiple Partitions Aligned Clustering](https://mlsanity.com/paper/1909.06008/) (2019)
- [Highly-Economized Multi-View Binary Compression for Scalable Image Clustering](https://mlsanity.com/paper/1809.05992/) (2018)
- [Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering](https://mlsanity.com/paper/2301.02484/) (2023)

## Abstract (arXiv)

In recent years, anchor and hash-based multi-view clustering methods have gained attention for their efficiency and simplicity in handling large-scale data. However, existing methods often overlook the interactions among multi-view data and higher-order cooperative relationships during projection, negatively impacting the quality of hash representation in low-dimensional spaces, clustering performance, and sensitivity to noise. To address this issue, we propose a novel approach named Tensor-Interacted Projection and Cooperative Hashing for Multi-View Clustering(TPCH). TPCH stacks multiple projection matrices into a tensor, taking into account the synergies and communications during the projection process. By capturing higher-order multi-view information through dual projection and Hamming space, TPCH employs an enhanced tensor nuclear norm to learn more compact and distinguishable hash representations, promoting communication within and between views. Experimental results demonstrate that this refined method significantly outperforms state-of-the-art methods in clustering on five large-scale multi-view datasets. Moreover, in terms of CPU time, TPCH achieves substantial acceleration compared to the most advanced current methods. The code is available at textcolorredurlhttps://github.com/jankin-wang/TPCH.