Doubly Stochastic Normalization for Spectral Clustering

Ron Zass, Amnon Shashua*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

In this paper we focus on the issue of normalization of the affinity matrix in spectral clustering. We show that the difference between N-cuts and Ratio-cuts is in the error measure being used (relative-entropy versus L1 norm) in finding the closest doubly-stochastic matrix to the input affinity matrix. We then develop a scheme for finding the optimal, under Frobenius norm, doubly-stochastic approximation using Von-Neumann's successive projections lemma. The new normalization scheme is simple and efficient and provides superior clustering performance over many of the standardized tests.

Original languageAmerican English
Title of host publicationNIPS 2006
Subtitle of host publicationProceedings of the 19th International Conference on Neural Information Processing Systems
EditorsBernhard Scholkopf, John C. Platt, Thomas Hofmann
PublisherMIT Press Journals
Pages1569-1576
Number of pages8
ISBN (Electronic)0262195682, 9780262195683
StatePublished - 2006
Event19th International Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, Canada
Duration: 4 Dec 20067 Dec 2006

Publication series

NameNIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems

Conference

Conference19th International Conference on Neural Information Processing Systems, NIPS 2006
Country/TerritoryCanada
CityVancouver
Period4/12/067/12/06

Bibliographical note

Publisher Copyright:
© NIPS 2006.All rights reserved

Fingerprint

Dive into the research topics of 'Doubly Stochastic Normalization for Spectral Clustering'. Together they form a unique fingerprint.

Cite this