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Erschienen in: Soft Computing 1/2012

01.01.2012 | Original Paper

Fuzzy local maximal marginal embedding for feature extraction

verfasst von: Cairong Zhao, Zhihui Lai, Chuancai Liu, Xingjian Gu, Jianjun Qian

Erschienen in: Soft Computing | Ausgabe 1/2012

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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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Metadaten
Titel
Fuzzy local maximal marginal embedding for feature extraction
verfasst von
Cairong Zhao
Zhihui Lai
Chuancai Liu
Xingjian Gu
Jianjun Qian
Publikationsdatum
01.01.2012
Verlag
Springer-Verlag
Erschienen in
Soft Computing / Ausgabe 1/2012
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-011-0735-y

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