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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 56 57 58 59 60 61 62 | % Vl_GMM Learn a Gaussian Mixture Model using EM
% [MEANS, COVARIANCES, PRIORS] = VL_GMM(X, NUMCLUSTERS) fits a GMM with
% NUMCLUSTERS components to the data X. Each column of X represent a
% sample point. X may be either SINGLE or DOUBLE. MEANS, COVARIANCES, and
% PRIORS are respectively the means, the diagonal covariances, and
% the prior probabilities of the Guassian modes. MEANS and COVARIANCES
% have the same number of rows as X and NUMCLUSTERS columns with one
% column per mode. PRIORS is a row vector with NUMCLUSTER entries
% summing to one.
%
% [MEANS, COVARIANCES, PRIORS, LL] = VL_GMM(...) returns the
% loglikelihood (LL) of the model as well.
%
% [MEANS, COVARIANCES, PRIORS, LL, POSTERIORS] = VL_GMM(...) returns
% the posterior probabilities POSTERIORS of the Gaussian modes given
% each data point. The POSTERIORS matrix has NUMCLUSTERS rows and
% NUMDATA columns.
%
% VL_GMM() supports different initialization and optimization
% methods. Specifically, the following options are supported:
%
% Verbose::
% Increase the verbosity level (may be specified multiple times).
%
% Initialization:: RAND
% RAND initializes the means as random data poitns and the
% covaraince matrices as the covariance of X. CUSTOM allow
% specifying the initial means, covariances, and prior
% probabilities.
%
% InitMeans:: none
% Specify the initial means (size(X,1)-by-NUMCLUSTERS matrix).
%
% InitPriors:: none
% Specify the initial weights (a vector of dimension NUMCLUSTER).
%
% InitCovariances:: none
% Specify the initial diagonal covariance matrices
%
% NumRepetitions:: 1
% Number of times to restart EM. The solution with maximum
% loglikelihood is returned.
%
% CovarianceBound:: 10e-6
% Set the lower bound on the diagonal covariance values.
% The bound can be either a scalar or a vector with one
% entry per dimension. Using null bounds is possible, but
% may yield degenerate solutions, including NaNs.
%
% Example::
% VL_GMM(X, 10, 'verbose', 'MaxNumIterations', 20) estimates the
% mixture of 10 gaussians using at mosst 20 iterations.
%
% See also: <a href="matlab:vl_help('gmm')">GMMs</a>, VL_KMEANS(), VL_HELP().
% Authors: David Novotny and Andrea Vedaldi
% Copyright (C) 2013 David Novotny and Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
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