Java: Data Science Made Easy

Dimensionality reduction

Another group of unsupervised learning algorithms is dimensionality reduction algorithms. This group of algorithms compresses the dataset, keeping only the most useful information. If our dataset has too much information, it can be hard for a machine learning algorithm to use all of it at the same time. It may just take too long for the algorithm to process all the data and we would like to compress the data, so processing it takes less time. 

There are multiple algorithms that can reduce the dimensionality of the data, including Principal Component Analysis (PCA), Locally linear embedding, and t-SNE. All these algorithms are examples of unsupervised dimensionality reduction techniques.

Not all dimensionality reduction ...