Machine Learning: End-to-End guide for Java developers

Feature analysis and dimensionality reduction

Among the first tools to master are the different feature analysis and dimensionality reduction techniques. As in supervised learning, the need for reducing dimensionality arises from numerous reasons similar to those discussed earlier for feature selection and reduction.

A smaller number of discriminating dimensions makes visualization of data and clusters much easier. In many applications, unsupervised dimensionality reduction techniques are used for compression, which can then be used for transmission or storage of data. This is particularly useful when the larger data has an overhead. Moreover, applying dimensionality reduction techniques can improve the scalability in terms of memory and computation ...