1. Zoltán Szabó
  2. ITE


ITE / code / IPA / demos / estimate_PNL_ISA.m

function [W_hat,e_hat,de_hat] = estimate_PNL_ISA(x,ICA,ISA,unknown_dimensions,de,gaussianization)
%Estimates the PNL-ISA model. Method: gaussianization + ISA on the result of the gaussianization.
%   x: x(:,t) is the observation at time t.
%   ICA: solver for independent component analysis, see 'estimate_ICA.m'.
%   ISA: solver for independent subspace analysis (=clustering of the ICA elements). ISA.cost_type, ISA.cost_name, ISA.opt_type: cost type, cost name, optimization type. Example: ISA.cost_type = 'sumH', ISA.cost_name = 'Renyi_kNN_1tok', ISA.opt_type = 'greedy' means that we use an entropy sum ISA formulation ('sumH'), where the entropies are estimated Renyi entropies via kNN methods ('Renyi_kNN_1tok') and the optimization is greedy; see also 'demo_ISA.m'
%   unknown_dimensions: '0' means 'the subspace dimensions are known'; '1' means 'the number of the subspaces are known' (but the individual dimensions are unknown).
%   de: 
%       1)in case of 'unknown_dimensions = 0': 'de' contains the subspace dimensions.
%       2)in case of 'unknown_dimensions = 1': the length of 'de' must be equal to the number of subspaces, but the coordinates of the vector can be arbitrary.
%   gaussianization: gaussianization method and its parameters, see 'estimate_gaussianization.m'
%   e_hat: e_hat(:,t) is the estimated ISA source at time t.
%   W_hat: estimated ISA demixing matrix.
%   de_hat: in case of known subspace dimensions ('unknown_dimensions = 0') de_hat = de; else it contains the estimated subspace dimensions; ordered increasingly.
%   Zoltan Szabo, Barnabas Poczos, Gabor Szirtes, and Andras Lorincz. Post Nonlinear Independent Subspace Analysis. International Conference on Artificial Neural Networks (ICANN), pages 677-686, 2007.
%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
%This file is part of the ITE (Information Theoretical Estimators) Matlab/Octave toolbox.
%ITE 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 3 of the License, or (at your option) any later version.
%This software 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 ITE. If not, see <http://www.gnu.org/licenses/>.

    [x_LIN_hat] = estimate_gaussianization(x,gaussianization);
%(linear) ISA:
    [e_hat,W_hat,de_hat] = estimate_ISA(x_LIN_hat,ICA,ISA,unknown_dimensions,de,size(x_LIN_hat,1));