In this section, to make the discussion concrete, only the dimensionality reduction using PCA and the LDA for topic modeling will be discussed for text clustering. Other algorithms for unsupervised learning will be discussed in Chapter 13, My Name is Bayes, Naive Bayes with some practical examples.
Unsupervised machine learning
Dimensionality reduction
Dimensionality reduction is the process of reducing the number of variables under consideration. It can be used to extract latent features from raw and noisy features or to compress data while maintaining the structure. Spark MLlib provides support for dimensionality reduction on the RowMatrix class. The most commonly used algorithms for reducing the dimensionality of data are...
