Imperial College London

ProfessorDavidHand

Faculty of Natural SciencesDepartment of Mathematics

Senior Research Investigator
 
 
 
//

Contact

 

+44 (0)20 7594 2843d.j.hand Website CV

 
 
//

Assistant

 

Mrs Louise Rowland +44 (0)20 7594 2843

 
//

Location

 

547Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Henrion:2013:10.1002/sam.11167,
author = {Henrion, M and Hand, DJ and Gandy, A and Mortlock, DJ and Henrion, M and Hand, DJ and Gandy, A and Mortlock, DJ and Henrion, M and Hand, DJ and Gandy, A and Mortlock, DJ},
doi = {10.1002/sam.11167},
journal = {STATISTICAL ANALYSIS AND DATA MINING},
pages = {53--72},
title = {CASOS: a Subspace Method for Anomaly Detection in High Dimensional Astronomical Databases},
url = {http://dx.doi.org/10.1002/sam.11167},
volume = {6},
year = {2013}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - We develop a novel algorithm for detecting anomalies. Our method has been developed to suit the challenging task of detecting anomalous sources in cross-matched astronomical survey data. Our algorithm computes anomaly scores in lower-dimensional subspaces of the data. By subspaces we mean, in this work, subsets of the original data variables. Our technique presents several advantages over existing methods: it can work directly on data with missing values; it addresses some of the problems posed by high-dimensional data spaces; it is less susceptible to a masking effect from irrelevant features; it can be easily adapted to suit specific needs and it allows an easier interpretation of why a given object has a high combined anomaly score. One drawback of our method is that it cannot detect outliers that are only apparent in high-dimensional spaces. Anomaly scores are computed using a nearest neighbor (NN) technique, but the algorithm works with any other method computing numerical anomaly scores. We demonstrate the properties of our algorithm and evaluate its performance on both simulated and real datasets. We show that it is capable of outperforming state-of-the-art, full-dimensional approaches in some situations. © 2012 Wiley Periodicals, Inc., A Wiley Company.
AU - Henrion,M
AU - Hand,DJ
AU - Gandy,A
AU - Mortlock,DJ
AU - Henrion,M
AU - Hand,DJ
AU - Gandy,A
AU - Mortlock,DJ
AU - Henrion,M
AU - Hand,DJ
AU - Gandy,A
AU - Mortlock,DJ
DO - 10.1002/sam.11167
EP - 72
PY - 2013///
SN - 1932-1864
SP - 53
TI - CASOS: a Subspace Method for Anomaly Detection in High Dimensional Astronomical Databases
T2 - STATISTICAL ANALYSIS AND DATA MINING
UR - http://dx.doi.org/10.1002/sam.11167
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000209525100005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
VL - 6
ER -