# ITE / code / IPA / demos / estimate_uMA_IPA_TCC.m

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55``` ```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. % %INPUT: % 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. %OUTPUT: % 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)). % %REFERENCE: % 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 ("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 . %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); %dim_reduction: if (size_A1 > size_A2) %undercomplete ISA dim_reduction = size_A2; else %size_A1 = size_A2 dim_reduction = size_A1; end %de: 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); ```