Generalized N-Dimensional Independent Component Analysis Based Multiple Feature Selection and Fusion
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概要
- 論文の詳細を見る
We proposed a multilinear independent component analysis framework called generalized N-dimensional ICA (GND-ICA) by extending the conventional linear ICA based on multilinear algebra. Unlike the linear ICA that only treats one-dimensional data, the proposed GND-ICA treats N-dimensional data as a tensor without any preprocess of data vectorization. We furthermore introduce two types of GND-ICA solutions and analysis their efficiency and effectiveness. As an application, the GND-ICA can be used for multiple feature fusion and representation for color image classification. Many features extracted from a given image are constructed as a tensor. The feature tensor can be effective represented by GND-ICA. Compared with conventional linear subspace learning methods, GND-ICA is capable of obtaining more distinctive representation for color image classification.
- 2012-05-10
著者
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Han Xianhua
Graduate School of Science and Engineering, Ritsumeikan University
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Ai Danni
Graduate School Of Science And Engineering Ritsumeikan University
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Han Xianhua
Graduate School Of Science And Engineering Ritsumeikan University
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Chen Yen-wei
Graduate School Of Engineering And Science Ritsumeikan University
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Duan Guifang
State Key Laboratory of CAD & CG, Zhejiang University:Graduate School of Science and Engineering, Ritsumeikan University
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