Unsupervised Learning

machine-learning

Definition

Unsupervised Learning

Unsupervised learning is a paradigm of machine learning where the objective is to infer the intrinsic structure, density, or latent representations of an unlabelled dataset. Formally, given an instance space and a dataset sampled i.i.d. from an unknown probability distribution , the learner seeks to identify patterns or structural properties of without the supervision of target labels.

Methodological Approaches

The learner identifies patterns through structure discovery, such as clustering, where discrete groups are identified by maximising intra-cluster similarity. Alternatively, representation learning identifies lower-dimensional manifolds or latent spaces that preserve the essential information content of the original distribution. These methods are often complemented by density estimation to model the underlying probability density function of the data.

Learning Algorithm

Definition

Unsupervised Learning Algorithm

An unsupervised learning algorithm is a learning algorithm that maps unlabelled training data to a specific hypothesis:

where:

• is the instance space.
• is the hypothesis class.

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