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