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Semi-supervised manifold alignment with multi-graph embedding

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Abstract

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).

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Acknowledgements

This work was funded in part by the National Natural Science Foundation of China (No.61572240, 61806086).

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, JiangSu University, JiangSu, 212013, China

    Chang-Bin Huang, Timothy Apasiba Abeo, Xiao-Zhen Luo, Xiang-Jun Shen, Jian-Ping Gou & De-Jiao Niu

  2. School of Applied Science, Tamale Technical University, Box 3ER, Tamale, Ghana

    Timothy Apasiba Abeo

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  1. Chang-Bin Huang
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  2. Timothy Apasiba Abeo
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Correspondence to Xiang-Jun Shen.

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Huang, CB., Abeo, T.A., Luo, XZ. et al. Semi-supervised manifold alignment with multi-graph embedding. Multimed Tools Appl 79, 20241–20262 (2020). https://doi.org/10.1007/s11042-020-08868-9

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  • DOI: https://doi.org/10.1007/s11042-020-08868-9

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