Centering Versus Scaling for Hubness Reduction View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Roman Feldbauer , Arthur Flexer

ABSTRACT

Hubs and anti-hubs are points that appear very close or very far to many other data points due to a problem of measuring distances in high-dimensional spaces. Hubness is an aspect of the curse of dimensionality affecting many machine learning tasks. We present the first large scale empirical study to compare two competing hubness reduction techniques: scaling and centering. We show that scaling consistently reduces hubness and improves nearest neighbor classification, while centering shows rather mixed results. Support vector classification is mostly unaffected by centering-based hubness reduction. More... »

PAGES

175-183

References to SciGraph publications

Book

TITLE

Artificial Neural Networks and Machine Learning – ICANN 2016

ISBN

978-3-319-44777-3
978-3-319-44778-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-44778-0_21

DOI

http://dx.doi.org/10.1007/978-3-319-44778-0_21

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1047482291


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