Kernel spectral clustering

Date(s) : 27/02/2017   iCal
14 h 00 min - 15 h 00 min

We consider the setting of performing spectral clustering in a Hilbert space. We show how spectral clustering, coupled with some preliminary change of representation in a reproducing kernel Hilbert space, can bring down the representation of classes to a low-dimensional space and we propose a new algorithm for spectral clustering that automatically estimates the number of classes.

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