ETRI Journal

  • 제40권5호
  • /
  • Pages.634-642
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  • 2018
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Projection spectral analysis: A unified approach to PCA and ICA with incremental learning

  • Kang, Hoon (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Lee, Hyun Su (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 투고 : 2017.03.04
  • 심사 : 2018.05.03
  • 발행 : 2018.10.01
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초록

Projection spectral analysis is investigated and refined in this paper, in order to unify principal component analysis and independent component analysis. Singular value decomposition and spectral theorems are applied to nonsymmetric correlation or covariance matrices with multiplicities or singularities, where projections and nilpotents are obtained. Therefore, the suggested approach not only utilizes a sum-product of orthogonal projection operators and real distinct eigenvalues for squared singular values, but also reduces the dimension of correlation or covariance if there are multiple zero eigenvalues. Moreover, incremental learning strategies of projection spectral analysis are also suggested to improve the performance.

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참고문헌

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