Distools User Guide, 2 Hour Course, 4 Day Course Computing Dissimilarities, Manipulation , Visualization Dissimilarity Matrix Classification, Dissimilarity Space, PE-Embedding, Evaluation
Computing Dissimilarities
This page belongs to the User Guide of the DisTools Matlab package. It describes some of its commands. Links to other pages are listed above. More information can be found in the pages of the PRTools User Guide. Links are given at the bottom of this page.
DisTools focuses on the analysis of dissimilarities. The computation of dissimilarities should arise from the application, e.g. dissimilarities between images, shapes, spectra or time signals. However, also special distances between objects in vector spaces. Next to the PRTools routine proxm
offers DisTools a similar routine with an extended set of distances:
. In the below table a list is presented of all possibilities for its parameter proxxm
type
.
|
'POLYNOMIAL' | 'P': SIGN(A*B').*(A*B').^P 'HOMOGENEOUS' | 'H': SIGN(A*B').*(A*B').^P 'EXP' | 'E': EXP(-(||A-B||)/P) 'EXP-SUM' | 'ES': SUM_Z SIGN(P(Z)) * EXP(-(||A-B||)/P(Z)) 'RBF' | 'R': EXP(-(||A-B||.^2)/(P*P)) 'RBF-SUM' | 'RS': SUM_Z SIGN(P(Z)) * EXP(-(||A-B||.^2)/(P(Z)^2)) 'SIGMOID' | 'S': SIGM(A*B'/P) 'DSIGMOID' | 'DS': SIGM(||A-B||^(2P(1))/P(2)) 'DISTANCE' | 'D': ||A-B||.^P 'MULTIQUADRIC' | 'MQ': SQRT(||A-B||.^2/P(1) + P(2)) 'THIN-PLATE' | 'TP': ||A-B||.^(2*P(1))/P(2) * LOG(1+||A-B||.^(2*P(1))/P(2)) 'N-THIN-PLATE' | 'NTP': -||A-B||.^(2*P(1))/P(2) * LOG(1+||A-B||.^(2*P(1))/P(2)) 'MINKOWSKI' | 'M': SUM(|A-B|^P).^(1/P) 'CITY-BLOCK' | 'C': SUM(|A-B|) 'COSINE' | 'O': 1 - (A*B')/||A||*||B|| 'FOURIER' | 'F' 'TANH' | 'T': TANH(P*(A*B')/LENGTH(A) + P); 'ANOVA' | 'A': ANOVA MODEL 'BSPLINE' | 'B': BSPLINE MODEL, ORDER P 'ANOVABSPLINE' | 'AB': ANOVA-BSPLINE MODEL, ORDER P 'ANOVASPLINE1' | 'AS1':ANOVA-SPLINE MODEL, ORDER 1 'ANOVASPLINE2' | 'AS2':ANOVA-SPLINE MODEL, ORDER 2 'ANOVASPLINE3' | 'AS3':ANOVA-SPLINE MODEL, ORDER 3 |
PRTools User Guide
elements: datasets, datafiles. cells and doubles, mappings, classifiers, mapping types
operations: datasets, datafiles, cells and doubles, mappings, classifiers, stacked, parallel, sequential, dyadic
commands: datasets, representation, classifiers, evaluation, clustering and regression, examples, support