8 Fundamental Unsupervised Learning Algorithms
This chapter covers
- Dirichlet-Process K-Means
- Gaussian Mixture Models (GMMs)
- Dimensionality Reduction
In the previous chapters, we looked at supervised algorithms for classification and regression. This chapter focuses on unsupervised learning algorithms. Unsupervised learning takes place when no training labels are available. In this case, we are interested in discovering patterns in data and learning data representations. Applications of unsupervised learning span from clustering customer segments in e-commerce to extracting features from image data. In this chapter, we'll start by looking at the Bayesian non-parametric extension of the K-means algorithm followed by the EM algorithm for Gaussian Mixture Models (GMMs). We will then look at two different dimensionality reduction techniques, namely PCA and t-SNE in application to learning an image manifold. The algorithms in this chapter were selected for their mathematical depth and usefulness in real-world applications.