[10] | 1 | % DisTools Table of Contents
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| 2 | % 23-Nov-2009_17:27
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| 3 | %
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| 4 | % This Matlab toolbox for the analysis of dissimilarity data works only
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| 5 | % if also the pattern recognition toolbox PRTools is available.
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| 6 | % See http://prtools.org
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| 7 | %
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| 8 | % E. Pekalska, ela.pekalska@googlemail.com, University of Manchester
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| 9 | % R.P.W. Duin, r.duin@ieee.org, Delft University of Technology
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| 10 | %
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| 11 | % Characterization of dissimilarity matrices
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| 12 | % ------------------------------------------
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| 13 | % CHECKEUCL Check whether a square dissimilarity matrix has a Euclidean behavior
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| 14 | % CHECKTR Check whether a square dissimilarity matrix obeys triangle inequality
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| 15 | % CHARDMAT Fiand several characteristic of (dis)similarity data
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| 16 | % CORRTR Correct a square dissimilarity matrix to obey the triangle inequality
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| 17 | % DISCHECK Dissimilarity matrix check
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| 18 | % DISNORM Normalization of a dissimilarity matrix
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| 19 | % DISSTAT Basic statistics of the dissimilarity matrix
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| 20 | % ISSQUARE Check whether a matrix is square
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| 21 | % ISSYM Check whether a matrix is symmetric
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| 22 | % ASYMMETRY Compute asymmetry of dissimilarity matrix
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| 23 | % NNE Leave-one-out Nearest Neighbor error on a dissimilarity matrix
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| 24 | % NNERR Exact expected NN error from a dissimilarity matrix
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| 25 | %
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| 26 | % Dissimilarity Measures
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| 27 | % -----------------------------------------------
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| 28 | % COSDISTM Distance matrix based on inner products
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| 29 | % EUDISTM Euclidean distance matrix
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| 30 | % HAMDISTM Hamming distance matrix between binary vectors
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| 31 | % HAUSDM Hausdorff and modified Hausdorff distance between datasets of image blobs
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| 32 | %
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| 33 | % Transformations
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| 34 | % DISSIMT Fixed DISsimilarity-SIMilarity transformation
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| 35 | % MAKESYM Make a matrix symmetric
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| 36 | % PE_EM Pseudo-Euclidean embedding (includes Classical Scaling as a special case)
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| 37 | %
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| 38 | % Classification in Pseudo-Euclidean Space and indefinite kernels
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| 39 | % -----------------------------------------------
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| 40 | % SETSIG Set PE signature for mappings or datasets
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| 41 | % GETSIG Set PE signature for mappings or datasets
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| 42 | % ISPE_DATASET Test dataset for PE signature setting
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| 43 | % ISPE_EM Test mapping for PE signature setting
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| 44 | % PE_DISTM Square pseudo-Euclidean distance between two datasets
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| 45 | % PE_KERNELM Compute kernel in PE space
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| 46 | % PE_MTIMES Matrix multiplication (inner product) in PE space
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| 47 | % PE_PARZENC Parzen classifier in PE space
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| 48 | % PE_KNNC KNN classifier in PE space
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| 49 | % PE_NMC Nearest mean classifier in PE space
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| 50 | % PE_EM Pseudo-Euclidean linear embedding
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| 51 | % PLOTSPECTRUM Plot spectrum of eigenvalues
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| 52 | %
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| 53 | % Routines supporting in learning from dissimilarity matrices
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| 54 | % -----------------------------------------------------------------------
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| 55 | % CROSSVALD Cross-validation error for dissimilarity representations
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| 56 | % DISSPACES Compute various spaces out of a dissimilarity matrix
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| 57 | % GENDDAT Generate random training and test sets for dissimilarity data
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| 58 | % GENREP Generate a representation set
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| 59 | % GENREPI Generate indices for representation, learning and testing sets
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| 60 | % SELCDAT Select Class Subset from a Square Dissimilarity Dataset
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| 61 | % PROTSELFD Forward prototype selection
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| 62 | % DLPC LP-classifier on dissimilarity (proximity) data
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| 63 | % KNNDC K-Nearest Neighbor classifier for dissimilarity matrices
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| 64 | % PARZENDDC Parzen classifier for dissimilarity matrices
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| 65 | % TESTKD Test k-NN classifier for dissimilarity data
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| 66 | % TESTPD Test Parzen classifier for dissimilarity data
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| 67 | %
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| 68 | % EXAMPLES
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| 69 | % --------
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| 70 | % CROSSVALD_EX Crossvalidation of several classifiers
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