# Advanced Dimensionality Reduction in R

Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM.

4 Hours16 Videos51 Exercises4,241 Learners

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## Course Description

Dimensionality reduction techniques are based on unsupervised machine learning algorithms and their application offers several advantages. In this course you will learn how to apply dimensionality reduction techniques to exploit these advantages, using interesting datasets like the MNIST database of handwritten digits, the fashion version of MNIST released by Zalando, and a credit card fraud detection dataset. Firstly, you will have a look at t-SNE, an algorithm that performs non-linear dimensionality reduction. Then, you will also explore some useful characteristics of dimensionality reduction to apply in predictive models. Finally, you will see the application of GLRM to compress big data (with numerical and categorical values) and impute missing values. Are you ready to start compressing high dimensional data?
1. 1

### Introduction to Advanced Dimensionality Reduction

Free

Are you ready to become a master of dimensionality reduction? In this chapter, you'll start by understanding how to represent handwritten digits using the MNIST dataset. You will learn what a distance metric is and which ones are the most common, along with the problems that arise with the curse of dimensionality. Finally, you will compare the application of PCA and t-SNE .

Exploring the MNIST dataset
50 xp
Exploring MNIST dataset
100 xp
Digits features
100 xp
Distance metrics
50 xp
Euclidean distance
100 xp
Minkowski distance
100 xp
KL divergence
100 xp
PCA and t-SNE
50 xp
Generating PCA from MNIST sample
100 xp
t-SNE output from MNIST sample
100 xp
2. 2

### Introduction to t-SNE

Now, you will learn how to apply the t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm. After finishing this chapter, you will understand the different hyperparameters that have an impact on your results and how to optimize them. Finally, you will do something really cool: compute centroids prototypes of each digit to classify other digits.

3. 3

### Using t-SNE with Predictive Models

In this chapter, you'll apply t-SNE to train predictive models faster. This is one of the many advantages of dimensionality reduction. You will learn how to train a random forest with the original features and with the embedded features and compare them. You will also apply t-SNE to understand the patterns learned by a neural network. And all of this using a real credit card fraud dataset!

4. 4

### Generalized Low Rank Models (GLRM)

In the final chapter, you will practice another useful dimensionality reduction algorithm: GLRM. Here you will make use of the Fashion MNIST data to classify clothes, impute missing data and also train random forests using the low dimensional embedding.

Datasets

MNIST sampleCredit card fraudFashion MNIST sample

Collaborators

Federico Castanedo

Data Scientist at DataRobot

Federico Castanedo is the Lead Telco Data Scientist at DataRobot. He is also an O'Reilly author on data science. Previously, he was the Lead Data Scientist at Vodafone Group and before that Chief Data Scientist/Co-founder at Wise Athena. He has published several scientific papers about data fusion techniques, visual sensor networks, and machine learning. He holds a Ph.D. in Artificial Intelligence from the University Carlos III of Madrid and has also been a visiting researcher at Stanford University.