Small Codes for Big Signals

Abstract

This chapter explores a different kind of use of neural networks. Rather than classifying or detecting patterns directly, we try to build low dimensional representations of high dimensional signals. The simplest reason to do so is to build a map of a dataset. We’ve already seen one procedure for doing so. It turns out that procedure has problems; this chapter starts with two alternative procedures. These are useful in their own right for mapping datasets.