Sparse semi-supervised heterogeneous interbattery bayesian analysis. (December 2021)

• Record Type: Journal Article
• Authors: Sevilla-Salcedo, Carlos; Gómez-Verdejo, Vanessa; Olmos, Pablo M.
• Is Part Of: Pattern recognition. Volume 120(2021)
• Publication Date: 2021-12
• Subjects: Bayesian model -- Canonical correlation analysis -- Principal component analysis -- Factor analysis -- Feature selection -- Semi-supervised -- Multi-task; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108141
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel heterogeneous multiview bayesian model for factor analysis. Include Sparse capabilities to automatically select the most relevant features. Work in a Semisupervised way to handle unlabelled data as well as missing values. It outperforms most of the state-of-the-art algorithms. Results show great versatility and an interpretability gain. Graphical abstract: Abstract: The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate selection of the probabilities and priors of these bayesian models allows the model to better adapt to the data nature (i.e. heterogeneity, sparsity), obtaining a more representative latent space. The objective of this article is to propose a general FA framework capable of modelling any problem. To do so, we start from the Bayesian Inter-Battery Factor Analysis (BIBFA) model, enhancing it with new functionalities to be able to work with heterogeneous data, to include feature selection, and to handle missing values as well as semi-supervised problems. The performance of the proposed model, Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA), has been tested on different scenarios to evaluate each one of its novelties, showing not only a great versatility and an interpretability gain, but also outperforming most of the state-of-the-art algorithms.