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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Feature discovery in nonmetric pairwise data
 Journal of Machine Learning Research 5 (2004), No.2, pp.801818 ISSN: 15337928 ISSN: 15324435 

 English 
 Journal Article 
 Fraunhofer FIRST () 
Abstract
Pairwise proximity data, given as similarity or dissimilarity matrix, can violate metricity. This occurs either due to noise, fallible estimates, or due to intrinsic nonmetric features such as they arise from human judgments. So far the problem of nonmetric pairwise data has been tackled by essentially omitting the negative eigenvalues or shifting the spectrum of the associated (pseudo) covariance matrix for a subsequent embedding. However, little attention has been paid to the negative part of the spectrum itself. In particular no answer was given to whether the directions associated to the negative eigenvalues would at all code variance other than noise related. We show by a simple, exploratory analysis that the negative eigenvalues can code for relevant structure in the data, thus leading to the discovery of new features, which were lost by conventional data analysis techniques. The information hidden in the negative eigenvalue part of the spectrum is illustrated and discussed for three data sets, namely USPS handwritten digits, textmining and data from cognitive psychology.