CSAIL Event Calendar: Previous Series
Spectral clustering as optimization
Speaker: Marina Meila , University of Washington
Spectral clustering methods, i.e methods that use eigenvectors of a suitably chosen matrix to partition the data, have recently become popular. This talk will analyze from a novel perspective why spectral clustering works. In particular, we show that spectral algorithms work in a wider and more interesting range of cases than it is generally believed. In the vicinity of some special points called perfect, spectral clustering optimizes simultaneously two criteria: a dissimilarity measure akin to the isoperimetric number (that we call the multiway normalized cut) and a cluster coherence measure (that we call the gap).