Abstract
In graph-based linear dimensionality reduction algorithms, it is crucial to construct a neighbor graph that can correctly reflect the relationship between samples. This paper presents an improved algorithm called fuzzy local maximal marginal embedding (FLMME) for linear dimensionality reduction. Significantly differing from the existing graph-based algorithms is that two novel fuzzy gradual graphs are constructed in FLMME, which help to pull the near neighbor samples in same class nearer and nearer and repel the far neighbor samples of margin between different classes farther and farther when they are projected to feature subspace. Through the fuzzy gradual graphs, FLMME algorithm has lower sensitivities to the sample variations caused by varying illumination, expression, viewing conditions and shapes. The proposed FLMME algorithm is evaluated through experiments by using the WINE database, the Yale and ORL face image databases and the USPS handwriting digital databases. The results show that the FLMME outperforms PCA, LDA, LPP and local maximal marginal embedding.
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Acknowledgments
This work is partially supported by the Fujian Provincial Department of Science and Technology of China under grant no. JK2010046, JB10135, JA10226, 2009I0020. It is also partially supported by the National Science Foundation of China under grant no. 60472061, 60632050, 90820004 and Hi-Tech Research and Development Program of China under grant no. 2006AA04Z238. It is also partially supported by the Ministry of Industry and Information Technology of china under grant no. E0310/1112/JC01.
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Zhao, C., Lai, Z., Liu, C. et al. Fuzzy local maximal marginal embedding for feature extraction. Soft Comput 16, 77–87 (2012). https://doi.org/10.1007/s00500-011-0735-y
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DOI: https://doi.org/10.1007/s00500-011-0735-y