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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Apache Spark FREE CHAPTER 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Dimensionality reduction with principal component analysis


Dimensionality reduction is the process of reducing the number of dimensions or features. A lot of real data contains a very high number of features. It is not uncommon to have thousands of features. So we need to drill down to features that matter.

Dimensionality reduction serves several purposes, such as:

  • Data compression
  • Visualization

When the number of dimensions is reduced, it reduces the disk and memory footprint. Last but not least, it helps algorithms to run faster. It also helps reduce highly correlated dimensions to one.

Humans can only visualize three dimensions, but data has access to a much higher number of dimensions. Visualization can help find hidden patterns in a particular piece of data. Dimensionality reduction helps visualization by compacting multiple features into one.

The most popular algorithm for dimensionality reduction is principal component analysis (PCA).

Let's look at the following dataset:

Let's say the goal...

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