Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

TL;DR

This paper introduces a neural network with a novel robust subspace recovery layer integrated into an autoencoder for unsupervised anomaly detection, effectively distinguishing inliers from outliers based on subspace projections.

Contribution

It presents a new RSR layer that enhances autoencoders for anomaly detection by robustly extracting underlying subspaces and removing outliers in an unsupervised manner.

Findings

Achieves state-of-the-art precision and recall on image datasets.

Effectively distinguishes inliers from outliers using subspace distances.

Demonstrates robustness across diverse data types like images and documents.

Abstract

We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a "manifold" close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall.