聚类分析
计算机科学
代表(政治)
数据挖掘
维数(图论)
机器学习
高维数据聚类
趋同(经济学)
人工智能
过程(计算)
相关聚类
理论计算机科学
数学
政治
政治学
法学
纯数学
经济
经济增长
操作系统
作者
Xinhang Wan,Jiyuan Liu,Xiangchao Gan,Xinwang Liu,Siwei Wang,Yuzhen Wen,Tianjiao Wan,En Zhu
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-13
标识
DOI:10.1109/tnnls.2024.3378194
摘要
Multi-View clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most existing methods undergo high complexity, preventing them from being applied to large-scale tasks. Multi-View clustering via matrix factorization is a representative to address this issue. However, most of them map the data matrices into a fixed dimension, limiting the model's expressiveness. Moreover, a range of methods suffers from a two-step process, i.e., multimodal learning and the subsequent k -means, inevitably causing a suboptimal clustering result. In light of this, we propose a one-step multi-view clustering with diverse representation (OMVCDR) method, which incorporates multi-view learning and k -means into a unified framework. Specifically, we first project original data matrices into various latent spaces to attain comprehensive information and auto-weight them in a self-supervised manner. Then, we directly use the information matrices under diverse dimensions to obtain consensus discrete clustering labels. The unified work of representation learning and clustering boosts the quality of the final results. Furthermore, we develop an efficient optimization algorithm with proven convergence to solve the resultant problem. Comprehensive experiments on various datasets demonstrate the promising clustering performance of our proposed method. The code is publicly available at https://github.com/wanxinhang/OMVCDR.
科研通智能强力驱动
Strongly Powered by AbleSci AI