Projected hierarchical ALS for generalized Boolean matrix factorization - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Projected hierarchical ALS for generalized Boolean matrix factorization

Résumé

We introduce a versatile approach for Boolean factorization of binary data matrices based on a projected hierarchical alternating least squares method. The general model considered in this work allows for an arbitrary Boolean combination of the binary rank-1 terms. The underlying approximation problem is tackled by relaxing the binary constraints and representing the combining function by a multivariate polynomial. This leads to closed-form and simple to implement updates of the alternating algorithm. Performance comparisons with other methods from the literature are presented for the standard Boolean (‘OR’) mixture model. We also pro-vide results on real data, as well as factorization examples using XOR and 3-term majority logical operators as combining functions.
Fichier non déposé

Dates et versions

hal-04301894 , version 1 (23-11-2023)

Identifiants

Citer

Rodrigo Cabral Farias, Sebastian Miron. Projected hierarchical ALS for generalized Boolean matrix factorization. International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, Jun 2023, Rhodes Island, Greece. ⟨10.1109/ICASSP49357.2023.10094568⟩. ⟨hal-04301894⟩
4 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More