Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models

TL;DR

This paper introduces a novel deep Taylor decomposition method for one-class SVMs that explains anomaly predictions by decomposing them into input variable contributions, enhancing interpretability of anomaly detection models.

Contribution

The paper proposes a new approach that transforms one-class SVMs into neural networks and applies deep Taylor decomposition for interpretability, outperforming existing explanation methods.

Findings

Effective explanation of anomalies across various distance-based kernels

Outperforms sensitivity analysis, nearest neighbor, and edge detection baselines

Applicable to multiple common one-class SVM models

Abstract

A common machine learning task is to discriminate between normal and anomalous data points. In practice, it is not always sufficient to reach high accuracy at this task, one also would like to understand why a given data point has been predicted in a certain way. We present a new principled approach for one-class SVMs that decomposes outlier predictions in terms of input variables. The method first recomposes the one-class model as a neural network with distance functions and min-pooling, and then performs a deep Taylor decomposition (DTD) of the model output. The proposed One-Class DTD is applicable to a number of common distance-based SVM kernels and is able to reliably explain a wide set of data anomalies. Furthermore, it outperforms baselines such as sensitivity analysis, nearest neighbor, or simple edge detection.