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PAKDD
2015
ACM

Latent Space Tracking from Heterogeneous Data with an Application for Anomaly Detection

7 years 11 months ago
Latent Space Tracking from Heterogeneous Data with an Application for Anomaly Detection
Streaming heterogeneous information is ubiquitous in the era of Big Data, which provides versatile perspectives for more comprehensive understanding of behaviors of an underlying system/process. Human analysis of these volumes is infeasible, leading to unprecedented demands for mathematical tools which effectively parse and distill such data. However, the complicated nature of streaming heterogeneous data prevents the conventional multivariate data analysis methods being applied immediately. In this paper, we propose a novel framework together with an online algorithm, denoted as LSTH, for latent space tracking from heterogeneous data. Our method leverages the advantages of dimension reduction, correlation analysis and sparse learning to better reveal the latent relations among heterogeneous information and adapt to slow variations in streaming data. We applied our method on both synthetic and real data, and it achieves results competitive with or superior to the stateof-the-art in de...
Jiaji Huang, Xia Ning
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where PAKDD
Authors Jiaji Huang, Xia Ning
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