A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data

TL;DR

This paper introduces a biologically inspired neural network model for unsupervised anomaly detection in unordered data, leveraging principles from neurophysiology to improve detection accuracy and robustness.

Contribution

It presents a novel neuron-based neural network architecture inspired by visual processing in the brain for effective anomaly detection without parameter tuning.

Findings

Achieves high accuracy in univariate and multivariate anomaly detection

Displays properties like contrast invariance and dynamic thresholds similar to biological neurons

Operates without parameter tuning, simplifying deployment

Abstract

A fundamental problem in the field of unsupervised machine learning is the detection of anomalies corresponding to rare and unusual observations of interest; reasons include for their rejection, accommodation or further investigation. Anomalies are intuitively understood to be something unusual or inconsistent, whose occurrence sparks immediate attention. More formally anomalies are those observations-under appropriate random variable modelling-whose expectation of occurrence with respect to a grouping of prior interest is less than one; such a definition and understanding has been used to develop the parameter-free perception anomaly detection algorithm. The present work seeks to establish important and practical connections between the approach used by the perception algorithm and prior decades of research in neurophysiology and computational neuroscience; particularly that of information processing in the retina and visual cortex. The algorithm is conceptualised as a neuron model which forms the kernel of an unsupervised neural network that learns to signal unexpected observations as anomalies. Both the network and neuron display properties observed in biological processes including: immediate intelligence; parallel processing; redundancy; global degradation; contrast invariance; parameter-free computation, dynamic thresholds and non-linear processing. A robust and accurate model for anomaly detection in univariate and multivariate data is built using this network as a concrete application.