文件列表:

contents.m (1225, 2002-03-04)
fasticapearson.m (7545, 2002-03-04)
gbd_momentfit.m (4571, 2000-09-28)
gbd_score.m (2146, 2000-09-28)
gpl.txt (18347, 2000-09-28)
pearson_ica.m (4993, 2002-03-04)
pearson_ica_demo.m (10999, 2000-09-28)
pearson_momentfit.m (1851, 2002-03-04)
pearson_score.m (3726, 2002-03-04)

The Pearson-ICA package is Copyright (c) Helsinki University of Technology, Signal Processing Laboratory, Juha Karvanen, Jan Eriksson, 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: 1.2 Version date: October 9, 2001 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Description =========== This package provides the Matlab (5.x) functions needed for the use of the Pearson-ICA algorithm as described in J. Karvanen, J.Eriksson and V. Koivunen: "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 J. Karvanen and V. Koivunen: "Blind Separation Methods based on Pearson system and its Extensions" Signal Processing, 2002, to appear 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=

---

近期下载者：

---

相关文件：

---

评论：[我要评论] [举报此文件]

---

收藏者：