Unsupervised Learning (UL) methods are a class of machine learning which aims to disentangle the representations and reduce the dimensionality among the data without any predeﬁned labels. Among all UL methods, the Non-negative Matrix Factorization (NMF) factorizes the data into two subspaces of non-negative components. Moreover, the NMF enforces the non-negativity, sparsity, and part-based analysis, thus the representations can be interpreted and explained for the Explainable Artiﬁcial Intelligence (XAI) applications. However, one of the main issues when using the NMF is to impose the factorization rank r to identify the dimensionality of the subspaces, where the rank is usually unknown in advance and known as the non-negative rank that is used as a prior to carrying out the factorization. Accordingly, we propose a novel method for the non-negative rank r approximation to help solving this problem, and we generalize our method among different image scales. Where, the results on different image data sets conﬁrm the validity of our approach.
On approximating the non-negative rank: Applications to unsupervised image reduction / M. Abukmeil, S. Ferrari, A. Genovese, V. Piuri, F. Scotti - In: 2020 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)[s.l] : IEEE, 2020 Jun 22. - ISBN 9781728144337. - pp. 1-6 (( convegno 2020 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) tenutosi a Tunis nel 2020.
|Titolo:||On approximating the non-negative rank: Applications to unsupervised image reduction|
|Parole Chiave:||Unsupervised Learning; Explainable Artiﬁcial Intelligence (XAI); Non-negative Matrix Factorization, Non-negative Rank; Image Reduction|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/CIVEMSA48639.2020.9132972|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|