Psychologically Inspired, Rule-Based Outlier Detection in Noisy Data

Outlier detection is widely applied in several fields such as data mining, pattern recognition and bioinformatics. The algorithms used for outlier detection are based mainly on statistics and artificial intelligence. Our long-term goal is to study and apply the principles of human vision for solving outlier detection problems. Here we present an algorithm suitable for outlier detection based on the principles of Gestalt psychology. We demonstrate the algorithm's main properties on an example taken from human perception, the recognition of continuous curves formed of Gabor patches embedded into a noisy background. We show that the algorithm is tolerant with respect to added noise and is orientation independent. As a potential application we present the problem of filtering proteomics mass spectrometry data. The true peaks within a measured mass spectrum can be represented as a graph in which nodes are fragment peaks while edges represent equivalents of proximity, similarity and continuity defined in terms of chemical rules. The applicability of the principle to further problems is discussed.