Anomaly detection method spots shifts in mixed data features
Moment-Based Anomaly Detection in High-Dimensional Mixed Data
Machine Learning
Summary
Detecting unusual patterns in complex data with many different kinds of information is hard. The authors created a way to turn these mixed data types into a common format and generate scores that highlight which parts look suspicious. Their method combines these scores while considering how data features relate, helping to find strange changes in not just averages but also variability and shape. It works well on simulated tests and real biological and environmental data, and it helps explain which features caused an anomaly.
What this means in practice
- •For data scientists: Detect unusual patterns in datasets containing numbers and categories, identifying both sharp and subtle anomalies with interpretable feature scores.
- •For bioinformatics teams: Identify anomalies in multi-omics datasets that may indicate biological irregularities or experimental issues using moment-based aggregate scoring.
Authors
Davide Ferrari, Andrea Nicolodi
Abstract
We propose an anomaly detection method for high-dimensional mixed-type data that maps heterogeneous features to a common scale via standardized moment-based transformations, yielding interpretable feature-level anomaly scores. These scores are combined through a dependence-aware aggregation step to construct the Mixed-data Influence (MDI) score, accounting for correlations across features and providing a unified measure of how strongly an observation departs from the reference data distribution. By incorporating higher-order moments, the MDI can detect anomalies that arise not only from location shifts, but also from changes in variability, skewness, and other distributional characteristics. The aggregation mechanism allows our method to capture both sparse anomalies concentrated in a small number of features and weak, coordinated deviations distributed across many features. We empirically investigate the distribution and thresholding behavior of the MDI score across different sample sizes, dimensions, dependence structures, and data types. Simulation experiments and two real-data applications, to multi-omics and ESG data, evaluate finite-sample calibration, detection performance, and feature-level interpretability.