Dual Space Latent Representation Learning for Image Representation

Yulei Huang, Ziping Ma (), Huirong Li and Jingyu Wang
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Yulei Huang: School of Mathematics and Information Science, North Minzu University, Yinchuan 750030, China
Ziping Ma: School of Mathematics and Information Science, North Minzu University, Yinchuan 750030, China
Huirong Li: School of Mathematics and Computer Application, Shangluo University, Shangluo 726000, China
Jingyu Wang: School of Mathematics and Information Science, North Minzu University, Yinchuan 750030, China

Mathematics, 2023, vol. 11, issue 11, 1-25

Abstract: Semi-supervised non-negative matrix factorization (NMF) has achieved successful results due to the significant ability of image recognition by a small quantity of labeled information. However, there still exist problems to be solved such as the interconnection information not being fully explored and the inevitable mixed noise in the data, which deteriorates the performance of these methods. To circumvent this problem, we propose a novel semi-supervised method named DLRGNMF. Firstly, dual latent space is characterized by the affinity matrix to explicitly reflect the interrelationship between data instances and feature variables, which can exploit the global interconnection information in dual space and reduce the adverse impacts caused by noise and redundant information. Secondly, we embed the manifold regularization mechanism in the dual graph to steadily retain the local manifold structure of dual space. Moreover, the sparsity and the biorthogonal condition are integrated to constrain matrix factorization, which can greatly improve the algorithm’s accuracy and robustness. Lastly, an effective alternating iterative updating method is proposed, and the model is optimized. Empirical evaluation on nine benchmark datasets demonstrates that DLRGNMF is more effective than competitive methods.

Keywords: dual space; latent representation learning; semi-supervised non-negative matrix factorization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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