title: Explain Auto-Encoders to me please
body: <p>What I've read says that the output is the input, and that if you have an error trying to reconstruct an observation then it is anomalous. That makes sense to me. The problem I'm having is this:</p>
  
  <p>I'm familiar with supervised methods, so here's what I'm thinking. Normally when you make a model you have an observation, usually with many features, that lead to an outcome. You learn how these features relate to produce the outcome.</p>
  
  <p>For an auto-encoder, is it true that each observation uses n-1 features to predict the remaining feature? The model is created assuming some hidden structure and then it attempts to reconstruct it like that? </p>
  
  <p>Just a little confused, thanks  </p>