Generalized N-dimensional independent component analysis and its application to multiple feature selection and fusion for image classification

  • Authors:
  • Danni Ai;Guifang Duan;Xianhua Han;Yen-Wei Chen

  • Affiliations:
  • Graduate School of Science and Engineering, Ritsumeikan University Shiga 525-8577, Japan and Hitachi (China) Research & Development Corporation, Shanghai 200020, China;Graduate School of Science and Engineering, Ritsumeikan University Shiga 525-8577, Japan and State Key Laboratory of CAD & CG, Zhejiang University, Hangzhou 310058, China;Graduate School of Science and Engineering, Ritsumeikan University Shiga 525-8577, Japan;Graduate School of Science and Engineering, Ritsumeikan University Shiga 525-8577, Japan

  • Venue:
  • Neurocomputing
  • Year:
  • 2013

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Abstract

We propose a multilinear independent component analysis (ICA) framework called generalized N-dimensional ICA (GND-ICA) by extending the conventional linear ICA based on the 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 analyze 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 the conventional linear subspace learning methods, GND-ICA is capable of obtaining more distinctive representation for color image classification.