Journal
COMPLEXITY
Volume 2021, Issue -, Pages -Publisher
WILEY-HINDAWI
DOI: 10.1155/2021/6638985
Keywords
-
Funding
- National Science Foundation of China [61876213, 6177227, 61861033, 61976118, U1831127, 71972102]
- Key R&D Program Science Foundation in Colleges and Universities of Jiangsu Province [18KJA520005, 19KJA360001, 20KJA520002]
- Natural Science Fund of Jiangsu Province [BK20201397, BK20171494]
- National Key RD Program [2017YFC0804002, 2019YF B1404602]
- Natural Science Fund of Jiangxi Province [20202ACBL202007]
- Natural Science Foundation of Guangdong Province [2016A030307050]
- Special Foundation of Public Research of Guangdong Province [2016A020225008, 2017A040405062]
Ask authors/readers for more resources
A new and effective dimensional reduction method for face recognition, utilizing sparse graph embedding with fuzzy set for image classification, is proposed in the study. Experimental results demonstrate its superiority over other algorithms in various datasets.
In recent years, many face image feature extraction and dimensional reduction algorithms have been proposed for linear and nonlinear data, such as local-based graph embedding algorithms or fuzzy set algorithms. However, the aforementioned algorithms are not very effective for face images because they are always affected by overlaps (outliers) and sparsity points in the database. To solve the problems, a new and effective dimensional reduction method for face recognition is proposed-sparse graph embedding with the fuzzy set for image classification. The aim of this algorithm is to construct two new fuzzy Laplacian scattering matrices by using the local graph embedding and fuzzy k-nearest neighbor. Finally, the optimal discriminative sparse projection matrix is obtained by adding elastic network regression. Experimental results and analysis indicate that the proposed algorithm is more effective than other algorithms in the UCI wine dataset and ORL, Yale, and AR standard face databases.
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