A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning

Abstract

For more than a century, the methods for data representation and the exploration of the intrinsic structures of data have developed remarkably and consist of supervised and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high and the data can come in messy, incomplete, unlabeled, or corrupted forms. Consequently, discovering the hidden structure buried inside such data becomes highly challenging. From this perspective, exploratory data analysis plays a substantial role in learning the hidden structures that encompass the significant features of the data in an ordered manner by extracting patterns and testing hypotheses to identify anomalies. Unsupervised generative learning models are a class of machine learning models characterized by their potential to reduce the dimensionality, discover the exploratory factors, and learn representations without any predefined labels; moreover, such models can generate the data from the reduced factors’ domain. The beginner researchers can find in this survey the recent unsupervised generative learning models for the purpose of data exploration and learning representations; specifically, this article covers three families of methods based on their usage in the era of big data: blind source separation, manifold learning, and neural networks, from shallow to deep architectures.

Generative Artificial Intelligence: Trends and Prospects

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Machine Learning Methods in Real-World Studies of Cardiovascular Disease

“…The main task of unsupervised ML is to explore hidden data patterns and to group unlabeled data into sub-populations by clustering and/or dimensionality reduction with feature/variable selection. Because unsupervised learning methods can identify the underlying data structure without a need for human intervention, they are suitable for exploratory analysis [22].…”

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

“…Generally, there are different learning types of generative models like Gaussian mixture models (GMM; Reynolds, 2009), hidden Markov models (HMM; Eddy, 1996), latent Dirichlet allocation (LDA; Blei et al, 2003), Boltzmann machines (BM; Ackley et al, 1985), deep belief networks (DBNs; Hinton, 2009), fully visible belief networks (FVBNs; Frey, 1998), VAE (Doersch, 2016), and GANs (Goodfellow et al, 2014). There are various literature reviews providing comprehensive surveys on these different types of the generative models (e.g., Abukmeil et al, 2021;De et al, 2022;Goodfellow, 2017;Harshavardha et al, 2020;Strokach & Kim, 2022;Tomczak, 2022;Turhan & Bilge, 2018).…”