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Adaptive Symmetric NMF for the Clustering of Sets of Points

 

Organizing data into clusters is a key task for data compression and classi cation. In this note we consider the case where the data are points belonging to a linear space, whose distance is measured through the Euclidean norm. A symmetric nonnegative similarity matrix is obtained from the data and a symmetric nonnegative matrix factorization (SymNMF) is computed in an alternating framework, through a penalized nonsymmetric formulation. An adaptive strategy to deal with the penalization parameter is proposed and validated by the numerical experimentation.


2015

Autori esterni: Grazia Lotti (Dipart. di Matematica e Informatica, University of Parma), Ornella Menchi (Dipart. di Informatica, University of Pisa), Francesco Romani (Dipart. di Informatica, University of Pisa)
Autori IIT:

Tipo: TR Rapporti tecnici
Area di disciplina: Mathematics
IIT TR-16/2015

File: IIT TR-16-2015.pdf

Attività: Metodi numerici per problemi di grandi dimensioni