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Applied Unsupervised Learning with Python

You're reading from   Applied Unsupervised Learning with Python Discover hidden patterns and relationships in unstructured data with Python

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781789952292
Length 482 pages
Edition 1st Edition
Languages
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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 (12) Chapters Close

Applied Unsupervised Learning with Python
Preface
1. Introduction to Clustering FREE CHAPTER 2. Hierarchical Clustering 3. Neighborhood Approaches and DBSCAN 4. Dimension Reduction and PCA 5. Autoencoders 6. t-Distributed Stochastic Neighbor Embedding (t-SNE) 7. Topic Modeling 8. Market Basket Analysis 9. Hotspot Analysis Appendix

t-Distributed SNE


t-SNE aims to address the crowding problem using a modified version of the KL divergence cost function and by substituting the Gaussian distribution with the Student's t-distribution in the low-dimensional space. Student's t-distribution is a continuous distribution that is used when one has a small sample size and unknown population standard deviation. It is often used in the Student's t-test.

The modified KL cost function considers the pairwise distances in the low-dimensional space equally, while the student's distribution employs a heavy tail in the low-dimensional space to avoid the crowding problem. In the higher-dimensional probability calculation, the Gaussian distribution is still used to ensure that a moderate distance in the higher dimensions is still represented as such in the lower dimensions. This combination of different distributions in the respective spaces allows the faithful representation of datapoints separated by small and moderate distances.

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