Outlier Detection on Mixed-Type Data: An Energy-based Approach

TL;DR

This paper introduces an energy-based unsupervised outlier detection method for mixed-type data using a novel probabilistic model, demonstrating its effectiveness and scalability on synthetic and real datasets.

Contribution

The paper proposes a new outlier detection approach for mixed-type data based on Mv.RBM and free-energy, addressing a key challenge in heterogeneous data analysis.

Findings

The method is fast, scalable, and competitive with state-of-the-art techniques.

Proper handling of mixed data types improves outlier detection accuracy.

Free-energy derived from Mv.RBM effectively identifies low-density outliers.

Abstract

Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Handling mixed-type data in a disciplined way remains a great challenge. In this paper, we propose a new unsupervised outlier detection method for mixed-type data based on Mixed-variate Restricted Boltzmann Machine (Mv.RBM). The Mv.RBM is a principled probabilistic method that models data density. We propose to use \emph{free-energy} derived from Mv.RBM as outlier score to detect outliers as those data points lying in low density regions. The method is fast to learn and compute, is scalable to massive datasets. At the same time, the outlier score is identical to data negative log-density up-to an additive constant. We evaluate the proposed method on synthetic and real-world datasets and demonstrate that (a) a proper handling mixed-types is necessary in outlier detection, and (b) free-energy of Mv.RBM is a powerful and efficient outlier scoring method, which is highly competitive against state-of-the-arts.