som_distortion

som_distortion - 2 E = sum sum h(bmu(i),j) ||m(j) - x(i)||...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
SOM Toolbox Online documentation http://www.cis.hut.fi/projects/somtoolbox/ som_distortion [adm,admu,tdmu] = som_distortion(sM, D, arg1, arg2) SOM_DISTORTION Calculate distortion measure for the map. [adm,admu,tdmu] = som_distortion(sMap, D, [radius], ['prob']) adm = som_distortion(sMap,D); [adm,admu] = som_distortion(sMap,D); som_show(sMap,'color',admu); Input and output arguments: sMap (struct) a map struct D (struct) a data struct (matrix) size dlen x dim, a data matrix [radius] (scalar) neighborhood function radius to be used. Defaults to the last radius_fin in the trainhist field of the map struct, or 1 if that is missing. ['prob'] (string) If given, this argument forces the neigborhood function values for each map unit to be normalized so that they sum to 1. adm (scalar) average distortion measure (sum(dm)/dlen) admu (vector) size munits x 1, average distortion in each unit tdmu (vector) size munits x 1, total distortion for each unit The distortion measure is defined as:
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 2
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 2 E = sum sum h(bmu(i),j) ||m(j) - x(i)|| i j where m(i) is the ith prototype vector of SOM, x(j) is the jth data vector, and h(.,.) is the neighborhood function. In case of fixed neighborhood and discreet data, the distortion measure can be interpreted as the energy function of the SOM. Note, though, that the learning rule that follows from the distortion measure is different from the SOM training rule, so SOM only minimizes the distortion measure approximately. If the 'prob' argument is given, the distortion measure can be interpreted as an expected quantization error when the neighborhood function values give the likelyhoods of accidentally assigning vector j to unit i. The normal quantization error is a special case of this with zero incorrect assignement likelihood. NOTE: when calculating BMUs and distances, the mask of the given map is used. See also SOM_QUALITY, SOM_BMUS, SOM_HITS. [ SOM Toolbox online doc ]...
View Full Document

This note was uploaded on 05/23/2010 for the course CS 245 taught by Professor Dunno during the Spring '10 term at Aarhus Universitet.

Page1 / 2

som_distortion - 2 E = sum sum h(bmu(i),j) ||m(j) - x(i)||...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online