Characteristics of auto-associative MLP as a novelty detector
Byung-Ho Hwang, Sungzoon Cho · 2003
In novelty detection, one tries to discriminate abnormal patterns from normal patterns. As a two class pattern classification problem, novelty detection is quite difficult since in practice only normal patterns are available for training. Novel or abnormal patterns are very few or not available at all. Recently, an auto-associative MLP (AaMLP) has been shown to give a good performance. In this paper, we analyze the output characteristics of trained AaMLPs and show that the AaMLP is indeed a reliable solution for novelty detection. In particular, we prove why nonlinearity in the hidden layer is necessary for novelty detection.