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 essential, and allows them to be used as computational models for synaptic adaptation.
In unsupervised learning, the machine receives inputs but obtains neither supervised target outputs, nor rewards from its environment. It may seem somewhat mysterious to imagine what the machine could possibly learn given that it doesn't get any feedback from its environment. However, it is possible to develop a formal framework for unsupervised learning, based on the notion that the machine's goal is to build representations of the input that can be used for decision making, predicting future inputs, efficiently communicating the inputs to another machine, and so on. In a sense, unsupervised learning can be thought of as finding patterns in the data above and beyond what would be considered noise.
Some of the goals of unsupervised learning are as follows:
- Discovering useful structures in large data sets without requiring a target desired output
- Improving learning speed for inputs
- Building a model of the data vectors by assigning a score or probability to each possible data vector
