文件列表:

EM\EM_Introduction.ppt (728576, 2011-05-06)
EM\gmmbayestb-v1.0\Contents.m (5764, 2005-04-14)
EM\gmmbayestb-v1.0\getargs.m (961, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_demo1d.m (1262, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_demo2d.m (1262, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_ellipse.m (3742, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_em.m (4175, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_em_gauss.m (1045, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_em_init_km.m (1429, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_em_step.m (2176, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_em_step_partial.m (1055, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_kmeans.m (4057, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_mixgen.m (1643, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_plot2.m (76, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_rand_split.m (2889, 2005-04-14)
EM\gmmbayestb-v1.0\gmmbvl_sqdist.m (399, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_classify.m (3750, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_cmeans.m (879, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_cmvnpdf.m (1723, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_covfixer.m (2853, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_create.m (2072, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_decide.m (680, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_demo01.m (5164, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_em.m (6712, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_em_init_cmeans1.m (1019, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_em_init_cmeans2.m (1021, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_em_init_fcm1.m (1107, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_fj.m (10924, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_frac2lhood.m (1704, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_fracthresh.m (1576, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_gem.m (2803, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_generatehist.m (1681, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_hist.m (1306, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_lhood2frac.m (1992, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_mkcplx.m (853, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_normalize.m (665, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_pdf.m (915, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_version.m (204, 2005-04-14)
EM\gmmbayestb-v1.0\gmmb_weightprior.m (824, 2005-04-14)
EM\gmmbayestb-v1.0\htmldoc\alpha.png (273, 2005-04-14)
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GMMBayes Toolbox Gaussian mixture model learning and Bayesian classification Version 1.0 Authors: Pekka Paalanen Joni Kmrinen Jarmo Ilonen Website: http://www.it.lut.fi/project/gmmbayes/ A document describing the used algorithms and test results can be found on the website. Note that the document is specific to the GMMBayes Toolbox version 0.1. Information about density quantile can be found in [1] Paalanen, P., Kamarainen, J.-K., Ilonen, J., Klviinen, H., Feature Representation and Discrimination Based on Gaussian Mixture Model Probability Densities - Practices and Algorithms, Research Report 95, Lappeenranta University of Technology, Department of Information Technology, 2005. For description of package contents see Contents.m file. Descriptions of used data structures are at the end of this file. All Matlab m-files should contain sufficient documentation. For acknowledgements and copyright information see Contents.m file. Contents 1. Major changes 2. The new classifier API 3. Changelog 4. Data structures --- 1. MAJOR CHANGES --- - The gmmb_create and gmmb_classify APIs have changed in version 0.2 and 0.3. - A new API has replaced the gmmb_classify interface, which remains as an unsupported legacy interface, in version 0.3. - A new fractile system in version 0.3. Fractiles are called density quantiles in version 1.0. --- 2. THE NEW CLASSIFIER API --- Let's assume training is already done and you have a bayesS structure. How to proceed to classify new data? First we compute PDF values for each class from each data point: pdfmat = gmmb_pdf(data, bayesS); If you are doing the usual Bayesian classification, you will apply priors, normalize the sums and make the decisions: postprob = gmmb_normalize( gmmb_weightprior(pdfmat, bayesS) ); labels = gmmb_decide(postprob); The resulting labels vector will contain class indexes, as coming from bayesS struct array. If you instead want to minimize the Bayes risk, you can multiply the postprob matrix with a cost matrix (note that rows correspond to data points) and get the labels: [dummy, labels] = min(postprob*cost, [], 2); The new API supports also density quantiles. For quantile related operations you will need a histS structure matching the used PDFs; either generated from bayesS structure (recommended), or computed from training data with the gmmb_hist function. histS structure can be generated like this: histS = gmmb_generatehist(bayesS, N); Since histS is a vector approximation of several functions, you need to tell the amount of sampling: the number of generated random points N. N should be fairly big, at least in the order of thousands. The bigger N, the better approximation, but also bigger memory consumption. You can convert PDF values to density quantile values: fracmat = gmmb_lhood2frac(histS, pdfmat); You can threshold the data points with respect to uniform quantile value, i.e., you can identify outliers with respect to each distribution: outlier_mask = gmmb_fracthresh(pdfmat, histS, thr); If you want to do the Bayesian classification with thresholding, you can now do: postprob(outlier_mask) = 0; labels = gmmb_decide(postprob); --- 3. CHANGELOG --- *** FINAL RELEASE GMMBayes Toolbox v1.0 *** - Changed the documentation to correspond the terminology in [1] (in the beginning of this readme). Fractiles are now called density quantiles. - Software cooldown period is over, this is the final version. *** BUG FIXES in GMMBayes Toolbox v0.3 *** - The complex Gaussian PDF was incorrect; it is now fixed. Also the real and complex cases need to be handled separately where applicable. NOTE: the complex range is detected from training data or the bayesS struct field 'mu'. Complex training data should have abs(Im)>0, otherwise it is truncated to real values. - Missed a warning() call in gmmb_covfixer.m, changed to warning_wrap(). - gmmb_fj: fixed variable names related to covfixer and verbose output. - gmmbvl_kmeans: fixed several function call names. - gmmb_pdf() has been changed to be more friendly to use. - The field histS has been removed from the bayesS structure. The boolean parameter has been removed from gmmb_create API. - Some tweaks in gmmbvl_em_step_partial and gmmbvl_kmeans to prevent the Matlab C compiler from whining. Still, it won't work ascompiled C application because of problems with the version() function. *** NEW FEATURES in GMMBayes Toolbox v0.3 *** - The new classifier API, described in the previous section. gmmb_demo01 uses this new API. - gmmb_cmvnpdf.m has been replaced with our own optimized code. - The interface gmmb.m and newgmmb.m have been removed. - gmmb_classify: fractile thresholding is done before classification. This way the "trash class" is the last choice, not second. As gmmb_classify no longer relies on bayesS.histS, it will call gmmb_generatehist() to create a histS structure. - gmmb_em, gmmb_fj and gmmb_gem: Functions will now print warnings about possibly insufficient amount of training data, but it does not directly imply that the estimation could fail. The limit is quite loose. - gmmb_frac2lhood, gmmb_lhood2frac, gmmb_fracthresh: The new fractile system, performing mapping between PDF-values (thresholds) and fractiles (probability mass of areas having PDF-value over a threshold). The fractile system is good for finding outliers. - gmmb_generatehist: Generate a histS structure from bayesS structure with random sampling. The fractile system needs a histS structure to work. *** BUG FIXES in GMMBayes Toolbox v0.2 *** - gmmb_em: the convergence threshold is now relative log-likelihood change, like in the other estimation methods. - infinite loop fixes in several places including: covfixer2, gmmb_fj - finity checks added in several places including: covfixer2, gmmb_em, gmmb_fj - covfixer2 is partly rewritten: the case when a value on the diagonal is less or equal to zero. The minimum correction amount should now be large enough to fix the covariance matrix. - use of covfixer2 in gmmb_em and gmmb_fj: covfixer2 can modify the covariance matrix so that the change of log-likelihood is unpredictable; this could lead to premature stop of the algorithm. Now if a covariance matrix is fixed, the threshold stop condition is not evaluated on that iteration. To prevent from going into infinite estimate/fix loop, a maximum of 20 consecutive covariance fixes are allowed; otherwise the algorithm is stopped. - gmmb_demo01: when done classification, show the classification result, not the true classes of the test data. *** NEW FEATURES in GMMBayes Toolbox v0.2 *** - gmmb_fj: added option 'broken' that is enabled by default (the old behaviour). When enabled, the number of free parameters is computed based on real-valued data (means and covariance matrices). If disabled and if data is complex valued, use a different computation for the number of free parameters. See the project report on the website for more information about handling complex data. - extended algorithm logging capabilities added to the following functions: covfixer2, gmmb_em, gmmb_fj, gmmb_create - tested with Matlab R12, should be compatible. NOTE: one change has to be made into getargs.m if using Matlab R12; comment a line and uncomment another. It is documented there. - added field 'hist' to bayesS struct to allow "trash class" classification - Optional trash class classification. When using Bayesian classification, you can give a 'fractile' value that tells how much of a class' training data is to be classified into the class in one-class classification scenario. Value of 1 says that all training data should be classified as belonging to the class, but no sample that has a lower likelihood than the worst sample in the class training data should be classified as belonging to the class. In multiclass scenarios, the one-class classification is performed first for each of the known classes, and the classes with which the sample passed, are used in Bayesian classification. - gmmb_em: added two new c-means based EM initialization methods, so that EM estimation no longer requires the Fuzzy Logic Toolbox. - gmmb_fj: added 'maxloops' optional parameter to control the maximum iterations allowed in a CEM run. - gmmb_demo01: added an example of trash class classification. --- 4. DATA STRUCTURES --- Specification for the so called bayesS struct in gmmb_* functions. K number of classes D number of dimensions C number of GMM components (class specific, can vary) N number of data points bayesS - struct array 1 x K mu D x C mean vectors sigma D x D x C covariance matrices weight C x 1 component weights apriories 1 x 1 class a priori probability When referring to "data" and "type" parameters: data N x D matrix type N x 1 vector (class label, positive integer) type is assumed to have at least one of every integer 1, ..., K. histS - cell array 1 x K of vectors Each vector contains an ordered list of PDF values of points from the distribution k described by the k'th PDF. $Name: $ $Id: readme.txt,v 1.4 2005/04/14 10:33:34 paalanen Exp $

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