LOGSPLINE INDEPENDENT COMPONENT ANALYSIS

• 川口 淳

  久留米大学バイオ統計センター

• Truong Young K.

  Department of Biostatistics, The University North Carolina at Chapel Hill

抄録

Most recent maximum likelihood approaches to independent component analysis (ICA) are based on nonparametric density estimation. In this paper, we present an algorithm by using the logsplines approach to density estimation. The logarithmic source density functions are modeled by polynomial splines or a linear combination of B-splines with (a) parameters or coefficients of the B-splines estimated by maximizing the log-likelihood function, and (b) knots of the B-splines determined by a stepwise procedure so as to minimize the approximation errors in modeling the log-density functions. We showed in a comparative study that our new algorithm has performed very favorably when compared to several popular density estimation based procedures.

収録刊行物

• Bulletin of informatics and cybernetics

  Bulletin of informatics and cybernetics 43 83-94, 2011-12

  統計科学研究会

キーワード

• Blind Source Separation
• Independent Component Analysis
• Logspline Density Estimation
• Maximum Likelihood Estimation

詳細情報

• CRID: 1390290699825840256
• NII論文ID: 120005397802
• NII書誌ID: AA10634475
• DOI: 10.5109/1434313
• ISSN: 2435743X
• ISSN: 0286522X
• HANDLE: 2324/1434313
• 本文言語コード: en
• データソース種別:
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  • IRDB
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• 抄録ライセンスフラグ: 使用可