Unsupervised learning is a branch of machine learning whose algorithms reveal inferences from data without an explicit label (unlabeled data). The goal of such techniques is to extract hidden patterns and group similar data.
In these algorithms, the unknown parameters of interests of each observation (the group membership and topic composition, for instance) are often modeled as latent variables (or a series of hidden variables), hidden in the system of observed variables that cannot be observed directly, but only deduced from the past and present outputs of the system. Typically, the output of the system contains noise, which makes this operation harder.
In common problems, unsupervised methods are used in two main situations:
With labeled datasets to extract additional features to be processed by the classifier/regressor down to the processing chain. Enhanced by additional features, they may perform better.
With labeled or unlabeled datasets to extract some information...
