Abstract
Stable orthogonal local discriminant embedding (SOLDE) is a recently proposed dimensionality reduction method, in which the similarity, diversity and interclass separability of the data samples are well utilized to obtain a set of orthogonal projection vectors. By combining multiple features of data, it outperforms many prevalent dimensionality reduction methods. However, the orthogonal projection vectors are obtained by a step-by-step procedure, which makes it computationally expensive. By generalizing the objective function of the SOLDE to a trace ratio problem, we propose a stable and orthogonal local discriminant embedding using trace ratio criterion (SOLDE-TR) for dimensionality reduction. An iterative procedure is provided to solve the trace ratio problem, due to which the SOLDE-TR method is always faster than the SOLDE. The projection vectors of the SOLDE-TR will always converge to a global solution, and the performances are always better than that of the SOLDE. Experimental results on two public image databases demonstrate the effectiveness and advantages of the proposed method.
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This paper is supported by National Natural Science Foundation of China (No.61401471 and No.61501471); General Financial from the China Postdoctoral Science Foundation (No.2014M552589) and Special Financial from the China Postdoctoral Science Foundation (No.2015T81114).
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Yang, X., Liu, G., Yu, Q. et al. Stable and orthogonal local discriminant embedding using trace ratio criterion for dimensionality reduction. Multimed Tools Appl 77, 3071–3081 (2018). https://doi.org/10.1007/s11042-017-5022-1
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DOI: https://doi.org/10.1007/s11042-017-5022-1