@inproceedings{
qu2024an,
title={An Lightweight  Anchor-Based Incremental Framework to Multi-view Clustering},
author={Qian Qu and Xinhang Wan and Weixuan Liang and Jiyuan Liu and Yu Feng and Huiying Xu and Xinwang Liu and En Zhu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=AtJH9nJZ5O}
}
The rapid development of multi-media techniques boosts the emergence of multi-view data, and how to uncover its intrinsic structure and utilize it to conduct the subsequent downstream tasks is crucial in data analysis. Multi-view clustering is a representative to handle multi-view data. The anchor-based method has received widespread attention for excellent performance and low time complexity. However, existing methods encounter two drawbacks, cutting down their performance, i.e., the assumption of the availability of all views and limited interaction of anchor generation among views. In some scenes, views arrive sequentially, and storing them is challenging owing to the limited space/ privacy considerations, and the existing anchor-based MVC is unsuitable for this. Additionally, recent works fail to generate anchors with the guidance of other views, and it is tough to align the anchor graphs. To this end, we propose A Lightweight Anchor-Based Incremental Framework for Multi-view Clustering. Specifically, we first initialize an anchor graph with the assistance of $k$-means when a new view arrives. Then, the consensus one of the anchor graphs is updated by the newly collected view with a permutation matrix. Our proposed method is more capable of anchor alignment because, in incremental MVC, the anchor graphs of previous views could be listed as a reference to guide the generation of anchor graphs of the coming view. Furthermore, we design a three-step iterative and convergent strategy to solve the resultant problem. Notably, the proposed algorithm shows outstanding effectiveness and time/space efficiency in extensive experiments.
An Lightweight  Anchor-Based Incremental Framework to Multi-view Clustering
Qian Qu Xinhang Wan Weixuan Liang Jiyuan Liu Yu Feng Huiying Xu Xinwang Liu En Zhu