Unsupervised Learning: What is a Sports Car?

Abstract

This tutorial studies unsupervised learning methods. Unsupervised learning methods are techniques that aim at reducing the dimension of data (covariables, features), cluster cases with similar features, and graphically illustrate high dimensional data. These techniques do not consider response variables, but they are solely based on the features themselves by studying incorporated similarities. For this reason, these methods belong to the field of unsupervised learning methods. The methods studied in this tutorial comprise principal components analysis (PCA) and bottleneck neural networks (BNNs) for dimension reduction, K-means clustering, K-medoids clustering, partitioning around medoids (PAM) algorithm and clustering with Gaussian mixture models (GMMs) for clustering, and variational autoencoder (VAE), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), self-organizing maps (SOM) and Kohonen maps for visualizing high dimensional data.

Keywords: PCA, biplot, autoencoder, bottleneck neural network (BNN), K-means clustering, K-medoids clustering, PAM algorithm, EM algorithm, clustering with Gaussian mixture models (GMMs), t-SNE, UMAP, SOM, Kohonen maps

JEL Classification: C2, C38, C45, G22