Unsupervised Learning: What is a Sports Car?

52 Pages Posted: 22 Aug 2019

Date Written: August 19, 2019

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), bottleneck neural networks (BNNs), K-means clustering, K-medoids clustering, partitioning around medoids (PAM) algorithm, clustering with Gaussian mixture models (GMMs), variational autoencoder (VAE), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), self-organizing maps (SOM), Kohonen maps.

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

Suggested Citation

Rentzmann, Simon and Wuthrich, Mario V., Unsupervised Learning: What is a Sports Car? (August 19, 2019). Available at SSRN: https://ssrn.com/abstract=3439358 or http://dx.doi.org/10.2139/ssrn.3439358

Simon Rentzmann

AXA Switzerland ( email )

Switzerland

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

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