Sparse semi-supervised heterogeneous interbattery bayesian analysis

doi:10.1016/j.patcog.2021.108141

Sparse semi-supervised heterogeneous interbattery bayesian analysis

2021-12-01

6

OA

AI

C

Carlos Sevilla-Salcedo *

V

Vanessa Gómez-Verdejo

P

Pablo M. Olmos

DOI：10.1016/j.patcog.2021.108141

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A B S T R A C T 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-theart algorithms. (c) 2021 Elsevier Ltd. All rights reserved.

Keywords:

Bayesian model

Canonical correlation analysis

Principal component analysis

Factor analysis

Feature selection

Semi-supervised Multi-task

Sparse semi-supervised heterogeneous interbattery bayesian analysis

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