PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Independent Component Analysis for Modelling Improvement

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A common problem encountered in such disciples as statistics, data analysis, signal processing, data mining and time series analysis is finding a suitable model to explore existing dependencies. There are many models with different advantages and it happens that different good criteria indicate different models as an optimal solution. We need good criteria to choose the best model. But how to choose the best criterion? To avoid this disadvantage we propose an Independent Component Analysis for adopting many solutions from a large set of competitive models. Such a transformation seems to capture the essential structure of data solutions in many applications.
Rocznik
Tom
Strony
151--164
Opis fizyczny
Bibliogr. 17 poz., rys.
Twórcy
autor
  • Polska Telefonia Cyfrowa, Al. Jerozolimskie 181, 02-222 Warszawa, Poland
autor
  • Polska Telefonia Cyfrowa, Al. Jerozolimskie 181, 02-222 Warszawa, Poland
  • Polska Telefonia Cyfrowa, Al. Jerozolimskie 181, 02-222 Warszawa, Poland
Bibliografia
  • [1]Amari S.; Natural gradient works efficiently in learning, Neural Computation, 10, 1998, pp. 271-276.
  • [2]Amari S., Cichocki A.; Adaptive blind signal processing - neural network approaches, Proceedings IEEE, vol.86, No.10, Oct. 1998, pp. 2026-2048.
  • [3] Cardoso J.F.; High-order contrasts for independent component analysis, Neural Computation, vol. 11, no 1, Jan. 1999, pp. 157-192.
  • [4] Cichocki A., Amari S.; Adaptive Blind Signal and Image Processing, John Wiley, Chichester 2002.
  • [5] Cichocki A., Sabala I., Choi S., Orsier B., Szupiluk R.; Self adaptive independent component analysis for sub-Gaussian and super-Gaussian mixtures with unknown number of sources and additive noise, Proceedings of 1997 International Symposium on Nonlinear Theory and its Applications (NOLTA-97), vol. 2, Hawaii, USA, Dec. 1997, pp. 731-734.
  • [6] Comon P.; Independent component analysis, a new concept?, Signal Processing, Elsevier, 36(3), Apr. 1994, pp. 287-314.
  • [7[ Greene W. H.; Econometric analysis, Prentice Hall, NJ 2000.
  • [8]Hyvarinen A.; Survey on independent component analysis, Neural Computing Surveys 2, 1999, pp. 94-128.
  • [9]Hyvarinen A., Karhunen J., Oja E.; Independent Component Analysis, John Wiley, 2001.
  • [10]Hyvarinen A., Oja E.; Independent component analysis: algorithms and applications, Neural Networks, 13 (4-5), 2000, pp. 411-430.
  • [11]Haykin S.; Neural networks: a comprehensive foundation, Macmillan, New York 1994.
  • [12] Jolliffe I. T.; Principal Component Analysis, Springer Verlag, July, 2002.
  • 131 Kennedy R.L. (ed.), Lee Y., Van Roy B., Reed C., Lippman R.P.: Solving Data Mining Problems with Pattern Recognition, Prentice Hall, December 1997.
  • [14] Lee T.W., Girolami M., Bell A., Sejnowski T.; A Unifying Information Theoretic Framework for Independent Component Analysis, International Journal on Mathematical and Computer Modelling, (39), 2000, pp. 1—21.
  • [15] Mitchell T.; Machine Learning, McGraw-Hill, Boston 1997.
  • [16]Mitra S., Pal S.K., Mitra P.; Data Mining in Soft Computing Framework: A Survey, IEEE Transactions on Neural Networks, 13(1), 2002.
  • [17]Cruces S., Cichocki A., Castedo L.; An iterative inversion method for blind source separation, Proceedings of the 1st International Workshop on Independent Component Analysis and Blind Signal Separation (ICA'99), Assois, France, Jan. 1999, pp. 307-312.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ1-0019-0106
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.