TPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clustering
This work addresses efficiency and accuracy issues in multi-view clustering for large-scale data, representing an incremental improvement over existing anchor and hash-based methods.
The paper tackles the problem of low-quality hash representation and poor clustering performance in multi-view clustering by proposing TPCH, which uses tensor-interacted projection and cooperative hashing to capture higher-order multi-view information, resulting in significant outperformance over state-of-the-art methods on five large-scale datasets and substantial acceleration in CPU time.
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 \textcolor{red}{\url{https://github.com/jankin-wang/TPCH}}.