Other goals for unsupervised learning

(1st-Nov-2020)

It provides a compact, low-dimensional representation of the input. – High-dimensional inputs typically live on or near a low dimensional manifold (or several such manifolds). – Principal Component Analysis is a widely used linear method for finding a low-dimensional representation.

• It provides an economical high-dimensional representation of the input in terms of learned features. – Binary features are economical. – So are real-valued features that are nearly all zero.

• It finds sensible clusters in the input. – This is an example of a very sparse code in which only one of the features is non-zero

Regarding Neural Networks

• Inspired by our understanding of how the brain learns • Powerful tool for addressing typical machine learning tasks such as regression and classification • Perform exceptionally well in speech recognition and object detection in images