Scala for Machine Learning

Summary

This completes the overview of three of the most commonly used unsupervised learning techniques:

• K-means for clustering fully observed features of a model with reasonable dimensions
• Expectation-maximization for clustering a combination of observed and latent features
• Principal components analysis to transform and extract the most critical features in terms of variance for linear models

Manifold learning for non-linear models is a technically challenging field with great potential in terms of dynamic object recognition [4:18].

The key point to remember is that unsupervised learning techniques are used:

• By themselves to extract structures and associations from unlabelled observations
• As a preprocessing stage to supervised learning in reducing the ...