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Accelerated Independent Component Analysis Using Generalized Logarithm

Research Project

Project/Area Number 13680465
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeSingle-year Grants
Section一般
Research Field Intelligent informatics
Research InstitutionWaseda University

Principal Investigator

MATSUYAMA Yasuo  School of Science and Engineering, Professor, 理工学部, 教授 (60125804)

Project Period (FY) 2001 – 2002
Project Status Completed (Fiscal Year 2002)
Budget Amount *help
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2002: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 2001: ¥2,200,000 (Direct Cost: ¥2,200,000)
Keywordsindependent component analysis / convex divergence / momentum / functional magnetic resonance imaging / brain functional map / fast algorithm / f-ICA / fMRI / 脳機能解析
Research Abstract

Independent Component Analysis (ICA) is a method to estimate unknown independent components which generate observed signals. In this research, the convex divergence was selected as the performance criterion for the independence. This measure is the source of the generalized logarithm. The obtained algorithm is named the f-ICA. The f-ICA contains the minimum mutual information ICA as a special case. The f-ICA can be realized as (a) the momentum method which adds the previous increment, and (b) the look-ahead method which adds the estimated future increment. Both methods show several times faster speed than the minimum mutual information method at the cost of a few additional memory. Thus, the first part of this project was successful by giving the accelerated ICA algorithm and novel properties of statistical measures related to the generalized logarithm.
In addition to the theoretical sophistication, the following experimental results are successfully obtained in this project:
(i) In any ICA algorithms, permutation indeterminacy is unavoidable. Users are obliged to check every independent component after the convergence of the algorithm. The investigator presented a way to inject prior knowledge as a regularization term. By this method, the most important component always appears as the first one.
(ii) A software system was created, which is beyond a laboratory level, i.e., a more general user level.
(iii) By using the above software system, human brain's functional maps are successfully obtained; (a) the main area of moving image recognition (dorsal occipital cortex), and (b) a separation of V1 and V2 regions of visual areas.

Report

(3 results)
  • 2002 Annual Research Report   Final Research Report Summary
  • 2001 Annual Research Report
  • Research Products

    (19 results)

All Other

All Publications (19 results)

  • [Publications] Y.Matsuyama, N.Katsumata, S.Imahara: "Independent component analysis using convex divergence"Proc. Int. Conf. on Neural Networks. 3. 1173-1178 (2001)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y.Matsuyama, S.Imahara: "Independent component analysis by convex divergence minimization : Applications to brain fMRI analysis"Proc. Int. Conf. on Neural Information Processing. 1. 412-417 (2001)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y.Matsuyama, S.Imahara, N.Katsumata: "Optimization transfer for computational learning : A hierarchy from f-ICA and alpha-EM to their off springs"Proc. Int. Joint Conf. on Neural Networks. 3. 1883-1888 (2002)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y.Matsuyama, N.Katsumata, R.Kawamura: "Optimization transfer using convex divergence : f-ICA and alpha-EM algorithm with examples"Proc. Int. Symp. on Information Theory and Its Applications. 2. 667-670 (2002)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y.Matsuyama, R.Kawamura: "Supervised map ICA : Applications to brain functional MRI"Proc. Int. Conf. on Neural Information Processing. 5. 2259-2263 (2002)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y.Matsuyama: "The α-EM algorithm : Surrogate likelihood maximization using α-logarithmic information measures"IEEE Transactions on Information Theory. 49-3(印刷中). (2003)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y. Matsuyama, N. Katsumata and S. Imahara: "Independent component analysis using convex divergence"Proc. Int. Conf. on Neural Networks. Vol. 3. 1173-1178 (2001)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y. Matsuyama and S. Imahara: "Independent component analysis by convex divergence minimization: Applications to brain fMRI analysis"Proc. Int. Conf. on Neural Information Processing. Vol. 1. 412-417 (2001)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y. Matsuyama, S. Imahara and N. Katsumata: "Optimization transfer for computational learning: A hierarchy from f-ICA and alpha-EM to their offsprings"Proc. Int. Joint Conf. on Neural Networks. Vol. 3. 1883-1888 (2002)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y. Matsuyama, N. Katsumata and R. Kawamura: "Optimization transfer using convex divergence: f-ICA and alpha-EM with examples"Proc. Int. Symp. on Information Theory and Its Applications. Vol. 2. 667-670 (2002)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y. Matsuyama and R. Kawamura: "Supervised map ICA: Applications to brain functional MRI"Proc. Int. Conf. on Neural Information Processing. Vol. 5. 2259-2263 (2002)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y. Matsuyama: "The α-EM algorithm: Surrogate likelihood maximization using α-logarithmic information measures"IEEE Transactions on Information Theory. Vol. 49, No. 3 (in print). (2003)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      2002 Final Research Report Summary
  • [Publications] Y.Matsuyama, S.Imahara, N.Katsumata: "Optimization transfer for computational learning : A hierarchy from f-ICA and Alpha-EM to their offsprings"Proc. Int. Joint Conf. on Neural Networks. 3. 1883-1888 (2002)

    • Related Report
      2002 Annual Research Report
  • [Publications] Y.Matsuyama, N.Katsumata, R.Kawamura: "Optimization transfer using covex divergence : f-ICA and alpha-EM algorithin with examples"Proc. Int. Symp. on Information Theory and Its Applications. 2. 667-670 (2002)

    • Related Report
      2002 Annual Research Report
  • [Publications] Y.Matsuyama, R.Kawamura: "Supervised map ICA : Applications to brain functional MRI"Proc. Int. Conf. on Neural Information Processing. 5. 2259-2263 (2002)

    • Related Report
      2002 Annual Research Report
  • [Publications] Y.Matsuyama: "The α-EM algorithm : Surrogate likelihood maximization using α-logarithmic information measures"IEEE Transactions on Information Theory. 49-3(印刷中). (2003)

    • Related Report
      2002 Annual Research Report
  • [Publications] Y.Matsuyama et al.: "Independent component analysis by convex divergence minimization : Applications to brain fMRI analysis"Proc. Int. Joint Conf. on Neural Networks. 1. 412-417 (2001)

    • Related Report
      2001 Annual Research Report
  • [Publications] Y.Matsuyama et al.: "Independent component analysis using convex divergence"Proc. Int. Conf. on Neural Information Processing. 3. 1173-1178 (2001)

    • Related Report
      2001 Annual Research Report
  • [Publications] Y.Matsuyama et al.: "Convex divergence as a surrogate function for independence : The f-divergence ICA"Proc. 3rd Int. Wksp on ICA and BSS. 1. x-y (2001)

    • Related Report
      2001 Annual Research Report

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Published: 2001-04-01   Modified: 2016-04-21  

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