메뉴 건너뛰기




Volumn 7, Issue 4, 2008, Pages 447-468

Designing relevant features for continuous data sets using ICA

Author keywords

Classification; Feature subset selection; HRCT; Independent component analysis

Indexed keywords

AREAS OF INTERESTS; CLASSIFICATION; CLASSIFICATION ACCURACIES; COMPUTER TOMOGRAPHY IMAGES; CONSTRUCTION TECHNIQUES; CONTINUOUS DATUM; DATA SETS; FEATURE EXTRACTION TECHNIQUES; FEATURE SELECTIONS; FEATURE SPACES; FEATURE SUBSET SELECTION; HIGH RESOLUTIONS; HRCT; LARGE DATA SETS; MACHINE-LEARNING; MEDICAL IMAGE SEGMENTATIONS;

EID: 64349122717     PISSN: 14690268     EISSN: None     Source Type: Journal    
DOI: 10.1142/S1469026808002387     Document Type: Article
Times cited : (7)

References (46)
  • 1
    • 0011812771 scopus 로고    scopus 로고
    • Kernel independent component analysis
    • F. R. Bach and M. I. Jordan, Kernel independent component analysis, J. Mach. Learn. Res. 3 (2002) 1-48.
    • (2002) J. Mach. Learn. Res , vol.3 , pp. 1-48
    • Bach, F.R.1    Jordan, M.I.2
  • 2
    • 85156201490 scopus 로고    scopus 로고
    • Independent component analysis through product density estimation
    • T. Hastie and R. Tibshirani, Independent component analysis through product density estimation, Proc. Neural Inform. Process. Syst. (2002), pp. 649-656.
    • (2002) Proc. Neural Inform. Process. Syst , pp. 649-656
    • Hastie, T.1    Tibshirani, R.2
  • 4
    • 33745561205 scopus 로고    scopus 로고
    • An introduction to variable and feature selection
    • A. E. Guyon, An introduction to variable and feature selection, J. Mach. Learn. Res. 3 (2003) 1157-1182.
    • (2003) J. Mach. Learn. Res , vol.3 , pp. 1157-1182
    • Guyon, A.E.1
  • 5
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi and G. H. John, Wrappers for feature subset selection, Artif. Intell. 97 (1997) 273-324.
    • (1997) Artif. Intell , vol.97 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 6
    • 0017535866 scopus 로고
    • A branch and bound algorithm for feature subset selection
    • P. M. Narendra and K. Fukunaga, A branch and bound algorithm for feature subset selection, IEEE Trans. Comput. C-26(9) (1977) 917-922.
    • (1977) IEEE Trans. Comput , vol.C-26 , Issue.9 , pp. 917-922
    • Narendra, P.M.1    Fukunaga, K.2
  • 7
    • 0019094381 scopus 로고
    • A critical evaluation of intrinsic dimensionality algorithms
    • eds. E. S. Gelsema and L. N. Kanal North-Holland
    • N. Wyse, R. Dubes and A. K. Jain, A critical evaluation of intrinsic dimensionality algorithms, Pattern Recognition in Practice, eds. E. S. Gelsema and L. N. Kanal (North-Holland, 1980), pp. 415-425.
    • (1980) Pattern Recognition in Practice , pp. 415-425
    • Wyse, N.1    Dubes, R.2    Jain, A.K.3
  • 8
    • 84992726552 scopus 로고
    • Estimating attributes: Analysis and extensions of relief
    • I. Kononenko, Estimating attributes: Analysis and extensions of relief, Eur. Conf. Mach. Learn. (1994), pp. 171-182.
    • (1994) Eur. Conf. Mach. Learn , pp. 171-182
    • Kononenko, I.1
  • 12
    • 0022030443 scopus 로고
    • Feature selection for automatic classification of non-Gaussian data
    • I. Foroutan and J. Sklansky, Feature selection for automatic classification of non-Gaussian data, IEEE Trans. Syst. Man Cybernet. 17 (1987) 187-198.
    • (1987) IEEE Trans. Syst. Man Cybernet , vol.17 , pp. 187-198
    • Foroutan, I.1    Sklansky, J.2
  • 15
    • 85065703189 scopus 로고    scopus 로고
    • Correlation-based feature selection for discrete and numeric class machine learning
    • M. Hall, Correlation-based feature selection for discrete and numeric class machine learning, Proc. Seventeenth Int. Conf. Mach. Learn. (1999), pp. 359-366.
    • (1999) Proc. Seventeenth Int. Conf. Mach. Learn , pp. 359-366
    • Hall, M.1
  • 17
    • 85076744892 scopus 로고    scopus 로고
    • Independent component representations for face recognition
    • M. S. Bartlett, H. M. Lades and T. J. Sejnowski, Independent component representations for face recognition, Proc. SPIE 3299 (1998) 528-539.
    • (1998) Proc. SPIE , vol.3299 , pp. 528-539
    • Bartlett, M.S.1    Lades, H.M.2    Sejnowski, T.J.3
  • 20
    • 4544320095 scopus 로고    scopus 로고
    • Feature selection in the independent component subspace for face recognition
    • H. K. Ekenel and B. Sankur, Feature selection in the independent component subspace for face recognition, Pattern Recogn. Lett. 25 (2004) 1377-1388.
    • (2004) Pattern Recogn. Lett , vol.25 , pp. 1377-1388
    • Ekenel, H.K.1    Sankur, B.2
  • 21
    • 0141573236 scopus 로고    scopus 로고
    • Genetic algorithm applied to ICA feature selection
    • Y. Huang and S. Luo, Genetic algorithm applied to ICA feature selection, Proc. Int. Conf. Neural Networks (2003), pp. 704-707.
    • (2003) Proc. Int. Conf. Neural Networks , pp. 704-707
    • Huang, Y.1    Luo, S.2
  • 22
    • 23044533903 scopus 로고    scopus 로고
    • Feature subset selection in an ICA space
    • eds. M. T. E. Monferrer, F. Toledo and E. Golobardes
    • M. Bressan and J. Vitriá, Feature subset selection in an ICA space, Topics in Artificial Intelligence, Springer Verlag Series, eds. M. T. E. Monferrer, F. Toledo and E. Golobardes, Lecture Notes in Computer Science, Vol. 2504 (2002), pp. 196-206.
    • (2002) Topics in Artificial Intelligence, Springer Verlag Series , vol.2504 , pp. 196-206
    • Bressan, M.1    Vitriá, J.2
  • 23
    • 28844501055 scopus 로고    scopus 로고
    • Feature extraction using supervised independent component analysis by maximizing class distance
    • Y. Sakaguchi, S. Ozawa and M. Kotani, Feature extraction using supervised independent component analysis by maximizing class distance, IEEJ Trans. Electron. Inform. Syst. 124-C (2004).
    • (2004) IEEJ Trans. Electron. Inform. Syst , vol.124-C
    • Sakaguchi, Y.1    Ozawa, S.2    Kotani, M.3
  • 24
    • 0002715112 scopus 로고    scopus 로고
    • A probabilistic approach to feature selection - A filter solution
    • Bari, Italy
    • H. Liu and R. Setiono, A probabilistic approach to feature selection - A filter solution, in 13th Int. Conf. Machine Learning (ICML'96), Bari, Italy (1996), pp. 319-327.
    • (1996) 13th Int. Conf. Machine Learning (ICML'96) , pp. 319-327
    • Liu, H.1    Setiono, R.2
  • 25
    • 1942418470 scopus 로고    scopus 로고
    • S. Perkins, K. Lacker and J. Theiler, Grafting: Fast, incremental feature selection by gradient descent in function space, J. Mach. Learn., Special Issue on Variable and Feature Selection (2003) 1333-1356.
    • S. Perkins, K. Lacker and J. Theiler, Grafting: Fast, incremental feature selection by gradient descent in function space, J. Mach. Learn., Special Issue on Variable and Feature Selection (2003) 1333-1356.
  • 28
    • 0025388740 scopus 로고
    • Efficient, numerically stablized rank-one eigenstructure updating
    • R. D. DeGroat and R. Roberts, Efficient, numerically stablized rank-one eigenstructure updating, IEEE Trans. Acoustics, Speech Signal Process. 38(2) (1990) 301-316.
    • (1990) IEEE Trans. Acoustics, Speech Signal Process , vol.38 , Issue.2 , pp. 301-316
    • DeGroat, R.D.1    Roberts, R.2
  • 29
    • 0020189418 scopus 로고
    • Efficient calculation of primary images from a set of images
    • H. Murakami and B. V. K. V. Kumar, Efficient calculation of primary images from a set of images, IEEE Trans. Pattern Anal. Mach. Intell. 4(5) (1982) 511-515.
    • (1982) IEEE Trans. Pattern Anal. Mach. Intell , vol.4 , Issue.5 , pp. 511-515
    • Murakami, H.1    Kumar, B.V.K.V.2
  • 31
    • 0022013023 scopus 로고
    • On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix
    • E. Oja and J. Karhunen, On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix, J. Math. Anal. Appl. 106 (1985) 69-84.
    • (1985) J. Math. Anal. Appl , vol.106 , pp. 69-84
    • Oja, E.1    Karhunen, J.2
  • 32
    • 0024883243 scopus 로고
    • Optimal unsupervised learning in a single-layer linear feedforward neural network
    • T. D. Sanger, Optimal unsupervised learning in a single-layer linear feedforward neural network, IEEE Trans. Neural Networks 2 (1989) 459-473.
    • (1989) IEEE Trans. Neural Networks , vol.2 , pp. 459-473
    • Sanger, T.D.1
  • 35
    • 0000717513 scopus 로고    scopus 로고
    • A new learning algorithm for blind signal seperation
    • eds. D. D. Touretzky, M. C. Mozer and M. E. Hasselmo, MIT Press, Cambridge, MA
    • S. Amari, A. Cichocki and H. H. Yang, A new learning algorithm for blind signal seperation, in Advances in Neural Information Processing Systems, eds. D. D. Touretzky, M. C. Mozer and M. E. Hasselmo, Vol. 8 (MIT Press, Cambridge, MA, 1996).
    • (1996) Advances in Neural Information Processing Systems , vol.8
    • Amari, S.1    Cichocki, A.2    Yang, H.H.3
  • 36
    • 0346307721 scopus 로고    scopus 로고
    • A fixed point algorithm for independent component analysis
    • A. Hyvarinen and E. Oja, A fixed point algorithm for independent component analysis, Neural Comput. 9(7) (1997) 1483-1492.
    • (1997) Neural Comput , vol.9 , Issue.7 , pp. 1483-1492
    • Hyvarinen, A.1    Oja, E.2
  • 39
    • 33744584654 scopus 로고
    • Induction of decision trees
    • J. R. Quinlan, Induction of decision trees, Mach. Learn. 1 (1986) 81-106.
    • (1986) Mach. Learn , vol.1 , pp. 81-106
    • Quinlan, J.R.1
  • 40
    • 0025725905 scopus 로고
    • Instance-based learning algorithms
    • D. W. Aha, D. Kibler and K. Albert, Instance-based learning algorithms, Mach. Learn. 6 (1991) 37-66.
    • (1991) Mach. Learn , vol.6 , pp. 37-66
    • Aha, D.W.1    Kibler, D.2    Albert, K.3
  • 43
    • 33847272567 scopus 로고    scopus 로고
    • Dimension selection for feature selection and dimension reduction with principal and independent component analysis
    • I. Koch and K. Naito, Dimension selection for feature selection and dimension reduction with principal and independent component analysis, Neural Comput. 19 (2007) 513-545.
    • (2007) Neural Comput , vol.19 , pp. 513-545
    • Koch, I.1    Naito, K.2
  • 44
    • 64349112004 scopus 로고    scopus 로고
    • C. Blake and C. Merz, UCI repository of machine learning databases, http://www.ics. uci.edu/~mlearn/MLRepository.html, http:// www.kernel-machines.com/ (1998).
    • C. Blake and C. Merz, UCI repository of machine learning databases, http://www.ics. uci.edu/~mlearn/MLRepository.html, http:// www.kernel-machines.com/ (1998).
  • 46
    • 10044266234 scopus 로고    scopus 로고
    • Feature subset selection using ICA for classifying emphysema in HRCT images
    • Cambridge, UK
    • M. Prasad, A. Sowmya and I. Koch, Feature subset selection using ICA for classifying emphysema in HRCT images, in Proc. Int. Conf. Pattern Recognition 2004, Cambridge, UK (2004), pp. 515-519.
    • (2004) Proc. Int. Conf. Pattern Recognition 2004 , pp. 515-519
    • Prasad, M.1    Sowmya, A.2    Koch, I.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.