Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders

11/12/2018
by   Yasuhiro Ikeda, et al.
0

Anomaly detection using dimensionality reduction has been an essential technique for monitoring multidimensional data. Although deep learning-based methods have been well studied for their remarkable detection performance, their interpretability is still a problem. In this paper, we propose a novel algorithm for estimating the dimensions contributing to the detected anomalies by using variational autoencoders (VAEs). Our algorithm is based on an approximative probabilistic model that considers the existence of anomalies in the data, and by maximizing the log-likelihood, we estimate which dimensions contribute to determining data as an anomaly. The experiments results with benchmark datasets show that our algorithm extracts the contributing dimensions more accurately than baseline methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/18/2018

Anomaly Detection and Interpretation using Multimodal Autoencoder and Sparse Optimization

Automated anomaly detection is essential for managing information and co...
research
09/09/2019

A Flexible Framework for Anomaly Detection via Dimensionality Reduction

Anomaly detection is challenging, especially for large datasets in high ...
research
04/23/2021

Scalable Microservice Forensics and Stability Assessment Using Variational Autoencoders

We present a deep learning based approach to containerized application r...
research
03/06/2019

Explaining Anomalies Detected by Autoencoders Using SHAP

Anomaly detection algorithms are often thought to be limited because the...
research
05/03/2022

TracInAD: Measuring Influence for Anomaly Detection

As with many other tasks, neural networks prove very effective for anoma...
research
09/21/2022

Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data

Recent advances in Explainable AI (XAI) increased the demand for deploym...
research
11/03/2020

Heartbeat Diagnosis of Performance Anomaly in OpenMP Multi-Threaded Systems

This paper presents a novel heartbeat diagnosis regarding performance an...

Please sign up or login with your details

Forgot password? Click here to reset