Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 77, Issue 3, Pages 3071-3081Publisher
SPRINGER
DOI: 10.1007/s11042-017-5022-1
Keywords
Trace ratio criterion; Manifold learning; Dimensionality reduction; Diversity
Categories
Funding
- National Natural Science Foundation of China [61401471, 61501471]
- General Financial from the China Postdoctoral Science Foundation [2014M552589]
- Special Financial from the China Postdoctoral Science Foundation [2015T81114]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available