Semi-supervised manifold alignment with multi-graph embedding

被引:1
作者
Huang Chang-Bin [1 ]
Abeo, Timothy Apasiba [1 ,2 ]
Luo Xiao-Zhen [1 ]
Shen Xiang-Jun [1 ]
Gou Jian-Ping [1 ]
Niu De-Jiao [1 ]
机构
[1] JiangSu Univ, Sch Comp Sci & Commun Engn, Zhenjiang 212013, Jiangsu, Peoples R China
[2] Tamale Tech Univ, Sch Appl Sci, Box 3ER, Tamale, Ghana
基金
中国国家自然科学基金;
关键词
Semi-supervised; Manifold alignmen; Multi-graph embedding; Correspondence information; NETWORKS; FRAMEWORK;
D O I
10.1007/s11042-020-08868-9
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a novel semi-supervised manifold alignment approach via multiple graph embeddings (MA-MGE) is proposed. Different from the traditional manifold alignment algorithms that use a single graph embedding to learn the latent manifold structure of each data set, our approach utilizes multiple graph embeddings to learn a joint latent manifold structure. Therefore a composite manifold representation with complete and more useful information is obtained from each dataset through a dynamic reconstruction of multiple graphs. Also, an optimization strategy based on eigen-value solutions is provided. Experimental results on Protein, COIL-20 and Face-10 datasets demonstrate superior performance of the proposed method compared with the state-of-the-art methods, such as semi-supervised manifold alignment (SSMA), manifold alignment using Procrustes analysis (PAMA) and manifold alignment without correspondence (UNMA).
引用
收藏
页码:20241 / 20262
页数:22
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