NDLI: Physics-Based Anomaly Detection Defined on Manifold Space

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ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 11

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Efficiently Estimating Motif Statistics of Large Networks

A Framework for Hierarchical Ensemble Clustering

Toward Personalized Context Recognition for Mobile Users: A Semisupervised Bayesian HMM Approach

Anomaly Detection from Incomplete Data

User Vulnerability and Its Reduction on a Social Networking Site

Selecting the Right Correlation Measure for Binary Data

Physics-Based Anomaly Detection Defined on Manifold Space

Issue 1, September 2014

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 8

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ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 2

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 1

Physics-Based Anomaly Detection Defined on Manifold Space

Content Provider	ACM Digital Library
Author	Qin, Hong Yoo, Shinjae Yu, Dantong Huang, Hao
Copyright Year	2014
Abstract	Current popular anomaly detection algorithms are capable of detecting global anomalies but often fail to distinguish local anomalies from normal instances. Inspired by contemporary physics theory (i.e., heat diffusion and quantum mechanics), we propose two unsupervised anomaly detection algorithms. Building on the embedding manifold derived from heat diffusion, we devise Local Anomaly Descriptor (LAD), which faithfully reveals the intrinsic neighborhood density. It uses a scale-dependent umbrella operator to bridge global and local properties, which makes LAD more informative within an adaptive scope of neighborhood. To offer more stability of local density measurement on scaling parameter tuning, we formulate Fermi Density Descriptor (FDD), which measures the probability of a fermion particle being at a specific location. By choosing the stable energy distribution function, FDD steadily distinguishes anomalies from normal instances with any scaling parameter setting. To further enhance the efficacy of our proposed algorithms, we explore the utility of anisotropic Gaussian kernel (AGK), which offers better manifold-aware affinity information. We also quantify and examine the effect of different Laplacian normalizations for anomaly detection. Comprehensive experiments on both synthetic and benchmark datasets verify that our proposed algorithms outperform the existing anomaly detection algorithms.
Starting Page	1
Ending Page	39
Page Count	39
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/2641574
Volume Number	9
Issue Number	2
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-23
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Anomaly detection Laplace operator Heat diffusion Quantum mechanics
Content Type	Text
Resource Type	Article
Subject	Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

On the spectrum of well-defined restrictions and extensions for the Laplace operator

A New Anomaly Detection Algorithm Based on Quantum Mechanics

A remark on the Gaussian lower bound for the Neumann heat kernel of the Laplace–Beltrami operator

Anomaly Detection from Incomplete Data

Measuring system normality

Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach

Anomaly detection: A survey

REGULARITY OF THE HEAT OPERATOR ON A MANIFOLD WITH CYLINDRICAL ENDS

The laplace operator on normal homogeneous Riemannian manifolds

Physics-Based Anomaly Detection Defined on Manifold Space

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On the spectrum of well-defined restrictions and extensions for the Laplace operator

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