We face the problem of interpreting parts of a dataset as small selections of features. Particularly, we propose a novel masked non- negative matrix factorization algorithm which is used to explain data as a composition of interpretable parts which are actually hidden in them and to introduce knowledge in the factorization process. Numerical ex- amples prove the effectiveness of the proposed MNMF algorithm as a useful tool for Intelligent Data Analysis.

Part-based data analysis with Masked Non-negative Matrix Factorization

CASALINO, GABRIELLA;DEL BUONO, Nicoletta;MENCAR, CORRADO
2014-01-01

Abstract

We face the problem of interpreting parts of a dataset as small selections of features. Particularly, we propose a novel masked non- negative matrix factorization algorithm which is used to explain data as a composition of interpretable parts which are actually hidden in them and to introduce knowledge in the factorization process. Numerical ex- amples prove the effectiveness of the proposed MNMF algorithm as a useful tool for Intelligent Data Analysis.
2014
978-3-319-09152-5
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/69946
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 3
social impact