文件列表:

Pearson_ICA (0, 2010-11-26)
Pearson_ICA\contents.m (1223, 2000-09-28)
Pearson_ICA\fasticapearson.m (8725, 2000-09-28)
Pearson_ICA\gbd_momentfit.m (4571, 2000-09-28)
Pearson_ICA\gbd_score.m (2146, 2000-09-28)
Pearson_ICA\gpl.txt (18347, 2000-09-28)
Pearson_ICA\pearson_ica.m (4940, 2000-09-28)
Pearson_ICA\pearson_ica_demo.m (10999, 2000-09-28)
Pearson_ICA\pearson_momentfit.m (1534, 2000-09-28)
Pearson_ICA\pearson_score.m (1516, 2000-09-28)
Pearson_ICA\说明.txt (169, 2010-11-26)

The Pearson-ICA package is Copyright (c) Helsinki University of Technology, Signal Processing Laboratory, Jan Eriksson, Juha Karvanen, and Visa Koivunen. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Version: 1.1 Version date: September 25, 2000 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Description =========== This package provides the Matlab (5.x) functions needed for the use of the Pearson-ICA algorithm as described in Karvanen, J.,Eriksson, J., and Koivunen, V.: "Pearson System Based Method for Blind Separation", Proceedings of Second International Workshop on Independent Component Analysis and Blind Signal Separation, Helsinki 2000, pp. 585--590 The algorithm is proposed to solve the standard noiseless linear ICA problem, i.e. the ICA model is Y=AS, where the number of sources s_i is equal to the number of observations y_i. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Installation: Just put all files to a directory along Matlab's search path. ============ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Quick use: Type pearson_ica_demo for a demonstration. ========= %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Use: === 1) Pearson-ICA algorithm ~~~~~~~~~~~~~~~~~~~~~~~~ Suppose you have the ICA mixture in the matrix `mixedsig', where the different rows correspond to the different outputs. Then estimatedsig=pearson_ica(mixedsig) gives the estimated independent components as the rows of the matrix `estimatedsig'. The estimated mixing matrix A and estimated separation matrix W are obtained as [estimatedsig,A,W]=pearson_ica(mixedsig) There are also some optional parameters you can change: 'epsilon' Convergence criterion 'maxNumIterations' Maximum number of iterations 'borderBase', 'borderSlope' The border lines between the Pearson family and the tanh contrast. I.e. the Pearson is used if borderBase(1)+borderSlope(1)*skewness^2=< kurtosis=

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