DTA

Corso Ordinario Secondo Livello

MICHELIS, FILIPPO

etd-05222024-153144

Nonnegative Matrix Factorization with Sparse Inverse Covariance

Classe Scienze Sociali

SCIENZE ECONOMICHE E MANAGERIALI - SCIENZE ECONOMICHE E MANAGERIALI

relatore Prof.ssa CHIAROMONTE, FRANCESCA
Presidente Prof. IRALDO, FABIO
Tutor Prof. BELLINI, NICOLA
Membro Prof. FAGIOLO, GIORGIO
Membro Prof. TESTA, FRANCESCO
Membro Prof.ssa ANNUNZIATA, ELEONORA

• Nessuna parola chiave trovata

14/06/2024;

completa

Nonnegative matrix factorization (NMF) has become increasingly popular as a powerful method for clustering and latent factor modeling. It decomposes data into a combination of latent fundamental parts, making it effective for dimensionality reduction. Previous research has primarily focused on obtaining meaningful parts, by manifold regularization, sparseness constraints, or orthogonality, without ever modeling co-occurrence patterns directly. A new NMF method is presented that achieves meaningful parts considering how they co-occur. Attention is shifted to the relationships within parts by modeling the conditional independence relationships among them. To accomplish this, a novel regularization term is proposed that induces sparsity on the inverse covariance matrix of the latent factors. The method performance is assessed through extensive simulations and a real data case study.

Nome file	Dimensione
Thesis_N...iance.pdf	2.20 Mb
Contatta l'autore