Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

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International Conference on Learning Representa...(2019)

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TL;DR

A neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer) that seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace.

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.

Authors

Chieh-Hsin Lai

1 papers

Dongmian Zou

1 papers

Gilad Lerman

1 papers

References61 items

Generative Adversarial Networks

Robust Subspace Recovery with Adversarial Outliers

q-Space Novelty Detection with Variational Autoencoders

Deep Anomaly Detection Using Geometric Transformations

An Overview of Robust Subspace Recovery

Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

Published in

International Conference on Learning Representa...(2019)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Chieh-Hsin Lai

1 papers

Dongmian Zou

1 papers

Gilad Lerman

1 papers

References61 items

Generative Adversarial Networks

Robust Subspace Recovery with Adversarial Outliers

q-Space Novelty Detection with Variational Autoencoders

Deep Anomaly Detection Using Geometric Transformations

An Overview of Robust Subspace Recovery

Static and Dynamic Robust PCA and Matrix Completion: A Review

Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

Novelty Detection with GAN

Efficient GAN-Based Anomaly Detection

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Deep Subspace Clustering Networks

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Anomaly Detection with Robust Deep Autoencoders

Wasserstein Generative Adversarial Networks

A Well-Tempered Landscape for Non-convex Robust Subspace Recovery

Robust, Deep and Inductive Anomaly Detection

Improved Training of Wasserstein GANs

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Identity Mappings in Deep Residual Networks

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lp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${l_p}$$\end{document}-Recovery of the Most Significant Subspace Amon

Fast, Robust and Non-convex Subspace Recovery

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Two proposals for robust PCA using semidefinite programming

Robust PCA via Outlier Pursuit

Median K-Flats for hybrid linear modeling with many outliers

Anomaly detection: A survey

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R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization

The relationship between Precision-Recall and ROC curves

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Quantifying curvelike structures of measures by using L2 Jones quantities

A Framework for Robust Subspace Learning

LOF: identifying density-based local outliers

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Rectifiable sets and the Traveling Salesman Problem

2018) as a common approach for RSR

Variational Autoencoder based Anomaly Detection using Reconstruction Probability

MNIST handwritten digit database

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N Figure 14: AUC and AP scores for RSRAE and alternative formulations using Tiny Imagenet (images) and 20

Field of Study

Computer ScienceMathematics

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

International Conference on Learning Representations

Type

conference

URL

https://iclr.cc/

Alternate Names

Int Conf Learn Represent
ICLR