Unsupervised learning (machine learning)

Last revised by Dimitrios Toumpanakis on 3 May 2021

Unsupervised learning is one of the main types of algorithms used in machine learning

Unsupervised learning algorithms are used on datasets where output labels are not provided. Hence, instead of trying to predict a particular output for each input, these algorithms attempt to discover the underlying structure of the input data, grouping similar inputs together. 

An example of a simple unsupervised learning algorithm is k-nearest neighbor clustering

Another example of unsupervised learning which is highly applicable to radiology is generative learning. Generative learning is an unsupervised deep learning approach where unlabelled data is used to train a generative model which learns to generate data similar to that of the dataset. 

Autoencoders can be conceived as a variation of unsupervised learning, although technically the output labels are are the same as the input data.

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