@inproceedings{cac3880a988647908cd937625e627dfc,
title = "Doubly stochastic normalization for spectral clustering",
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 L 1 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.",
author = "Ron Zass and Amnon Shashua",
year = "2007",
language = "American English",
isbn = "9780262195683",
series = "Advances in Neural Information Processing Systems",
pages = "1569--1576",
booktitle = "Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference",
note = "20th Annual Conference on Neural Information Processing Systems, NIPS 2006 ; Conference date: 04-12-2006 Through 07-12-2006",
}