Latent Space Tracking from Heterogeneous Data with an Application for Anomaly Detection
Abstract
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 anonline 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 latentrelations among heterogeneous information and adapt to slow variations in streaming data. We applied our method on both synthetic and realdata, and it achieves results competitive with or superior to the state-of-the-art in detecting several different types of anomalies.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 2015
- Accession Number
- AD1015879
Entities
People
- Jiaji Huang
- Xia Ning