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Volumn 13, Issue 3, 2003, Pages 267-276

Projection techniques for nonlinear principal component analysis

Author keywords

Feature extraction; Intrinsic dimensionality; Least squares approximation; Projection pursuit; Radial basis functions; Self consistency

Indexed keywords


EID: 0346463261     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1024274801715     Document Type: Article
Times cited : (16)

References (19)
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  • 3
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  • 5
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  • 8
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  • 10
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    • Bayesian radial basis functions of variable dimension
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  • 11
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    • Projection pursuit
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  • 13
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    • Nonlinear principal component analysis using autoassociative neural networks
    • Kramer M.A. 1991. Nonlinear principal component analysis using autoassociative neural networks. American Institute of Chemical Engineers Journal 37(2): 233-243.
    • (1991) American Institute of Chemical Engineers Journal , vol.37 , Issue.2 , pp. 233-243
    • Kramer, M.A.1
  • 14
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    • On the use of nonlocal and non positive definite basis functions in radial basis function networks
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    • (1995) Fourth IEE International Conference on Artificial Neural Networks , pp. 206-211
    • Lowe, D.1
  • 15
    • 0031646493 scopus 로고    scopus 로고
    • Limitations of nonlinear PCA as performed with generic neural networks
    • Malthouse E.C. 1998. Limitations of nonlinear PCA as performed with generic neural networks. IEEE Transactions on Neural Networks 9(1): 165-173.
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    • Malthouse, E.C.1
  • 16
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    • Tools for two-dimensional exploratory projection pursuit
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    • Posse, C.1
  • 18
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    • An approach to non-linear principal components analysis using radially symmetric kernel functions
    • Webb A.R. 1996. An approach to non-linear principal components analysis using radially symmetric kernel functions. Statistics and Computing 6: 159-168.
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    • Webb, A.R.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.