论文标题
稀疏的半监督异质互构贝叶斯分析
Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis
论文作者
论文摘要
贝叶斯的特征提取方法（称为因子分析（FA））在机器学习中已广泛研究，以获得数据的潜在表示。这些贝叶斯模型的概率和先验的足够选择使该模型可以更好地适应数据性质（即异质性，稀疏性），从而获得了更具代表性的潜在空间。 本文的目的是提出一个能够建模任何问题的一般FA框架。为此，我们从贝叶斯间间因子分析（BIBFA）模型开始，通过新功能来增强它，以便能够使用异质数据，包括特征选择，处理缺失值以及半忽视的问题。 在4种不同的场景上测试了所提出的模型，稀疏半监督的异质间贝叶斯分析（SSHIBA）的性能，以评估其每个新颖性，不仅显示出了很大的多功能性和可解释性增长，而且还表现出了大多数较大的现状algorithms。
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, include feature selection, and 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 4 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.
