Practical Machine Learning Cookbook

An overview of unsupervised learning

Unsupervised learning studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. Unsupervised learning is important since it is likely to be much more common in the brain than supervised learning. For example, the activities of photoreceptors in the eyes are constantly changing with the visual world. They go on to provide all the information that is available to indicate what objects there are in the world, how they are presented, what the lighting conditions are, and so on. However, essentially none of the information about the contents of scenes is available during learning. This makes unsupervised methods ...