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Adaptive Symmetric NMF for graph clustering

Organizing data into clusters is a key task for data compression and classi cation. In this paper we consider the case where the data are points belonging to a linear space, whose distance is measured through the Euclidean norm. A symmetric modeling of the graph clustering problem is addressed and an algorithm is proposed, based on NMF (nonnegative matrix factorization) techniques applied to a penalized nonsymmetric minimization problem. The solution depends on several
parameters, whose choice is crucial. To overcome this difficulty, we suggest a heuristic approach which detects the best parameter values in an adaptive way. Extensive experimentation shows that the proposed algorithm is effective.


2016

Autori IIT:

Tipo: Rapporto Tecnico
Area di disciplina: Mathematics
IIT TR-05/2016

File: TR 05-2016.pdf

Attività: Algoritmica per tecnologie web