File(s) under permanent embargo
Outlier detection on mixed-type data: An energy-based approach
conference contribution
posted on 2016-01-01, 00:00 authored by Kien DoKien Do, Truyen TranTruyen Tran, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshOutlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Handling mixed-type data in a disciplined way remains a great challenge. In this paper, we propose a new unsupervised outlier detection method for mixed-type data based on Mixed-variate Restricted Boltzmann Machine (Mv.RBM). The Mv.RBM is a principled probabilistic method that models data density. We propose to use free-energy derived from Mv.RBM as outlier score to detect outliers as those data points lying in low density regions. The method is fast to learn and compute, is scalable to massive datasets. At the same time, the outlier score is identical to data negative log-density up-to an additive constant. We evaluate the proposed method on synthetic and real-world datasets and demonstrate that (a) a proper handling mixed-types is necessary in outlier detection, and (b) free-energy of Mv.RBM is a powerful and efficient outlier scoring method, which is highly competitive against state-of-the-arts.
History
Volume
10086 LNAIPagination
111 - 125Publisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319495859Publication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2016, Springer International PublishingTitle of proceedings
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Usage metrics
Categories
No categories selectedLicence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC