文献类型：期刊文献

英文题名：Efficient Extended-Graph Multi-Matrices Subspace Learning: Toward Meta-Similarity and Efficiency Promotion

作者：Ren, Maoye[1]; Yu, Xinhai[1]

机构：[1] Department of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2024

外文期刊名：SSRN

收录：EI(收录号：20240149943)

语种：英文

外文关键词：Gradient methods - Optimization

摘要：Multi-view learning (MVL) has become a hot topic due to it can view a problem from different perspectives and settle it comprehensively. The consensus and complementarity principles provide important guidances for MVL. A category of MVL methods, called multi-matrices learning (MML), can excavate more geometry information in a sample, and shows superior performance. However, existing MML models mainly follow only the consensus principle, through exploiting the label correlation in regularization terms. In this paper, we propose an efficient extended-graph multi-matrices subspace learning (EEGMMSL) approach for MML. The diagonal terms of our extended-graph can maintain the intro-view sample-similarity-relationships, embed samples of each view to a easier-separated-subspace, realizing the complementarity principle. The off-diagonal items of our extended-graph sustain the cross-view sample-similarity-relationships, and keep these embedded subspaces consensus, guaranteeing the consensus principle from sample level. By further maintaining the meta-similarity in our extended-graph, it can rectify the relationships in these subspaces, so that guarantees consensus principle from the feature level. Our approach can unify most of the relation-based MML methods and is more flexible in solving different problems. Focusing on the efficiency, we also proposed an new M-separate optimization algorithm to more efficiently optimize our objective function, instead of its traditional heuristic-gradient-descent optimization. This greatly reduces the computational complexity of MML. Experiments show the superior performance of EEGMMSL, and the remarkable time advantage of the M-separate optimization algorithm. ? 2024, The Authors. All rights reserved.