3. Unsupervised learning
The aim of "unsupervised" learning [H 5 012] is to extract information
and discover hidden structures from unannotated data: an unsupervised
learning algorithm therefore explores the data without any a priori
knowledge of the classes or expected results. The main unsupervised
learning methods are partitioning and dimensionality reduction, the
principles of which are briefly explained below.
Dimensionality reduction methods are a class of...
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Unsupervised learning