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Unsupervised Learning
This chapter delves into unsupervised learning models. In the previous chapter we
explored Autoencoders, novel neural networks that learn via unsupervised learning.
In this chapter we will delve deeper into some other unsupervised learning models.
In contrast to supervised learning, where the training dataset consists of both the
input and the desired labels, unsupervised learning deals with the case where the
model is provided only the input. The model learns the inherent input distribution
by itself without any desired label guiding it. Clustering and dimensionality
reduction are the two most commonly used unsupervised learning techniques.
In this chapter we will learn about different machine learning and NN techniques
for both. We will cover techniques required for clustering and dimensionality
reduction, and will go into the details of Boltzmann machines, and finally we will
cover the implementation of the aforementioned techniques using TensorFlow. The
concepts covered will be extended to build Restricted Boltzmann Machines (RBMs).
The chapter will include:
• Principal component analysis
• K-Means clustering
• Self-organizing maps
• Boltzmann machines
• RBMs
Principal component analysis
Principal component analysis (PCA) is the most popular multivariate statistical
technique for dimensionality reduction. It analyzes the training data consisting of
several dependent variables, which are, in general, inter-correlated, and extracts
important information from the training data in the form of a set of new orthogonal
variables called principal components. We can perform PCA using two methods
either using eigen decomposition or using singular value decomposition (SVD).
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