Fuzzy local maximal marginal embedding for feature extraction |
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Authors: | Cairong Zhao Zhihui Lai Chuancai Liu Xingjian Gu Jianjun Qian |
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Affiliation: | (1) School of Computer Science, Nanjing University of Science and Technology, Nanjing, 210094, Jiansu, China;(2) Department of Physics and Electronics, Minjiang College, Fuzhou, 350108, Fujian, China |
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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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