Enhanced MDL with application to atypicality
Elyas Sabeti, Anders Høst-Madsen · 2017
With the enormous amount of data generated through the internet and sensors, Internet of Things, it becomes too overwhelming for humans to examine it all. One solution is to reduce the data to a set of statistics. The perspective in this paper is the opposite, namely that most of this data is just background noise, and the interesting parts are those that deviate from background noise, the parts that are atypical. In order to find such “interesting” parts of data, universal approaches are required, since it is not known in advance what we are looking for. Our approach is to use Rissanen's minimum description length (MDL) as a tool for that. We would like to be able to find both short and long atypical sequences of data, and we therefore need accurate expressions of MDL, without prior assumptions. In this paper we develop a modified predictive MDL method that works better for short sequences.