Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

Conference Paper

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.

Full Text

Duke Authors

Cited Authors

  • Lai, C-H; Zou, D; Lerman, G

Published Date

  • May 1, 2020

Conference Name

  • International Conference on Learning Representations

Conference Location

  • Addis Ababa, Ethiopia

Conference Start Date

  • April 26, 2020

Conference End Date

  • May 1, 2020