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The Unsupervised Learning Workshop

You're reading from   The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800200708
Length 550 pages
Edition 1st Edition
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Authors (3):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Christopher Kruger Christopher Kruger
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Christopher Kruger
Aaron Jones Aaron Jones
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Aaron Jones
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Table of Contents (11) Chapters Close

Preface
1. Introduction to Clustering 2. Hierarchical Clustering FREE CHAPTER 3. Neighborhood Approaches and DBSCAN 4. Dimensionality Reduction Techniques and PCA 5. Autoencoders 6. t-Distributed Stochastic Neighbor Embedding 7. Topic Modeling 8. Market Basket Analysis 9. Hotspot Analysis Appendix

Stochastic Neighbor Embedding (SNE)

SNE is one of a number of different methods that fall within the category of manifold learning, which aims to describe high-dimensional spaces within low-dimensional manifolds or bounded areas. At first thought, this seems like an impossible task; how can we reasonably represent data in two dimensions if we have a dataset with at least 30 features? As we work through the derivation of SNE, it is hoped that you will see how this is possible. Don't worry – we will not be covering the mathematical details of this process in great depth as it is outside of the scope of this chapter. Constructing an SNE can be divided into the following steps:

  1. Convert the distances between datapoints in the high-dimensional space into conditional probabilities. Say we had two points, xi and xj, in a high-dimensional space and we wanted to determine the probability (pi|j) that xj would be picked as a neighbor of xi. To define this probability, we use...
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