%% DisTools Table of Contents %% 23-Nov-2009_17:27 %% %% This Matlab toolbox for the analysis of dissimilarity data works only %% if also the pattern recognition toolbox PRTools is available. %% See http://prtools.org %% %% E. Pekalska, ela.pekalska@googlemail.com, University of Manchester %% R.P.W. Duin, r.duin@ieee.org, Delft University of Technology %% %% Characterization of dissimilarity matrices %% ------------------------------------------ %% CHECKEUCL Check whether a square dissimilarity matrix has a Euclidean behavior %% CHECKTR Check whether a square dissimilarity matrix obeys triangle inequality %% CHARDMAT Fiand several characteristic of (dis)similarity data %% CORRTR Correct a square dissimilarity matrix to obey the triangle inequality %% DISCHECK Dissimilarity matrix check %% DISNORM Normalization of a dissimilarity matrix %% DISSTAT Basic statistics of the dissimilarity matrix % GOFCL Goodness of clusters/classes separability vs compactness for dissimilarity data % INTRDIM Estimate Intrinsic dimension from dissimilarity data %% ISSQUARE Check whether a matrix is square %% ISSYM Check whether a matrix is symmetric %% ASYMMETRY Compute asymmetry of dissimilarity matrix %% NEF Negative Eigen Fraction %% NNE Leave-one-out Nearest Neighbor error on a dissimilarity matrix %% NNERR Exact expected NN error from a dissimilarity matrix % NNERROR Exact expected NN error from a dissimilarity matrix (2) % VAT Visual Assessment of cluster Tendency for dissimilarity matrices % %% %% Dissimilarity Measures %% ----------------------------------------------- % BINDISTM Dissimilarity matrix between binary vectors % BLURDISTM Blurred Euclidean distance matrix between blobs % BLOBBOX Find box around a binary blob and resample % CORRDISTM Distance matrix based on correlations %% COSDISTM Distance matrix based on inner products % DPROCRUSTDM Distance matrix between datasets based on extended Procrustes problem % EDGEDISTM Distance matrix between images based on their edges % EDITDISTM Edit distance matrix between strings %% EUDISTM Euclidean distance matrix % EXPDISTM Exponential-type of distance matrix % FLPDISTM Fast computation of the lp (p > 0) distance matrix %% HAMDISTM Hamming distance matrix between binary vectors %% HAUSDM Hausdorff and modified Hausdorff distance between datasets of image blobs % JACSIMDISTM Jaccard-like distance matrix based on similarities % LPDISTM l_p (p > 0) distance matrix % QDISTM Distance matrix for quantitative variables % RANKDISTM Distance matrix between two data sets based on ranking % SAMDISTM Distance matrix based on Spectral Angular Mapper (SAM) % STRKERM String Kernel Matrix by Lodhi et al % %% %% Transformations and projections %% ----------------------------------------------- %% DISSIMT Fixed DISsimilarity-SIMilarity transformation % KCENTERM Kernel weighted centering mapping (also for a similarity matrix) %% MAKESYM Make a matrix symmetric % PROXXM Proximity mapping % SIGMOID Element-wise sigmoid tranformation of a matrix % FASTMAPD FastMap; inear projection of Euclidean distances %% PE_EM Pseudo-Euclidean embedding (includes Classical Scaling as a special case) % SPHEM Spherical Embedding % %% %% Classification in Pseudo-Euclidean Space and indefinite kernels %% ----------------------------------------------- % PE_AFFINE %% SETSIG Set PE signature for mappings or datasets %% GETSIG Set PE signature for mappings or datasets %% ISPE_DATASET Test dataset for PE signature setting %% ISPE_EM Test mapping for PE signature setting %% PE_DISTM Square pseudo-Euclidean distance between two datasets %% PE_KERNELM Compute kernel in PE space % PE_LIBSVC Libsvc for PE spaces %% PE_MTIMES Matrix multiplication (inner product) in PE space %% PE_PARZENC Parzen classifier in PE space %% PE_KNNC KNN classifier in PE space %% PE_NMC Nearest mean classifier in PE space %% PE_EM Pseudo-Euclidean linear embedding of dissimilarities % KEM Kernel embedding %% PLOTSPECTRUM Plot spectrum of eigenvalues % PSPCA Pseudo-Euclidean Principal Component Analysis % % Indefinte kernel routines % ------------------------- % IKFD Indefinite Kernel Fisher discriminant % IKPCA Indefinite Kernel PCA % %% %% Routines supporting in learning from dissimilarity matrices %% ----------------------------------------------------------------------- %% CROSSVALD Cross-validation error for dissimilarity representations % CLEVALD Classifier evaluation (Learning curve) %% DISSPACES Compute various spaces out of a dissimilarity matrix %% GENDDAT Generate random training and test sets for dissimilarity data %% GENREP Generate a representation set %% GENREPI Generate indices for representation, learning and testing sets %% SELCDAT Select Class Subset from a Square Dissimilarity Dataset %% PROTSELFD Forward prototype selection % AUCDLPC AUC-LP classifier on dissimilarity data %% DLPC LP-classifier on dissimilarity (proximity) data % DRSSCC Dissimilarity-based random subspace combining classifier %% KNNDC K-Nearest Neighbor classifier for dissimilarity matrices %% PARZENDDC Parzen classifier for dissimilarity matrices % KFD Kernel Fisher Discriminant % KSVC Kernel Support Vector classifier on a kernel matrix % KSVO Kernel Support Vector Optimizer % KSVC_NU Kernel Support Vector classifier on a kernel matrix; nu-version % KSVO_NU Kernel Support Vector Optimizer; nu-version % MCLASSDC Multi-Class Dissimilarity-based Classifier from Two-Class Discriminants %% TESTKD Test k-NN classifier for dissimilarity data %% TESTPD Test Parzen classifier for dissimilarity data % TQDC Trade-off Quadratic Discriminant (Regularized Bayes Normal Classifier) % % % Graphs and distances % ----------------------------------------------- % DISTGRAPH Computes distances in a graph % DMSTSPM Finds the shortest paths along K minimum spanning trees % DSPATH Single shortest path in a (dissimilarity) graph % DSPATHS All shortest paths in a (dissimilarity) Graph % GRAPHPATH Compute shortest paths in a graph % KMST Finds K minimum spanning trees based on a distance matrix % MSTPLOT Plot minimum spanning trees % NHGRAPH Find a neighborhood graph and its shortest paths % PLOTGRAPH Plot a 2D graph %% %% EXAMPLES %% -------- %% CROSSVALD_EX Crossvalidation of several classifiers % Superfluous / outdated but still available % ------------------------------------------------- % KPCA, AUGPSEM, PSEM %