ITE / code / IPA / demos / estimate_uMA_IPA_TCC.m

function [e_hat,W_hat,de_hat,L2] = estimate_uMA_IPA_TCC(x,L,ICA,ISA,unknown_dimensions,de)
%Estimates the uMA-IPA model. Method: temporal concatenation of the observations + ISA.
%   x: x(:,t) is the observation at time t.
%   L: length of the convolution; H_0,...,H_{L}: L+1 H_j matrices.
%   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.
%   e_hat: e_hat(:,t) is the estimated source at time t.
%   W_hat: estimated 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.
%   L2: determines (i) the number times the subspaces are recovered (L+L2), (ii) the dimension of the associated ISA task (De x (L+L2)).
%   Zoltan Szabo, Barnabas Poczos, and Andras Lorincz. Undercomplete Blind Subspace Deconvolution. Journal of Machine Learning Research 8(May):1063-1095, 2007.
%Copyright (C) 2012 Zoltan Szabo ("", "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 <>.

%dimension of the observation (Dx) and the source (De):
    Dx = size(x,1);
    De = sum(de);    
%L2-concatenated observation (X):
    L2 = ceil(De*L/(Dx-De));%minimal possible L' (for which an undercomplete system is obtained)
    X = concatenation_d(x,1,L2);

%ISA on X:
    %size of the underlying TCC mixing matrix:
        size_A1 = Dx * L2;
        size_A2 = De * (L+L2);
        if (size_A1 > size_A2) %undercomplete ISA 
            dim_reduction = size_A2;
        else %size_A1 = size_A2
            dim_reduction = size_A1;
        de = kron(de,ones(L+L2,1));%de is a column vector
    [e_hat,W_hat,de_hat] = estimate_ISA(X,ICA,ISA,unknown_dimensions,de,dim_reduction);