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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. An anomaly detection algorithm is further developed as an application of learning latent space. We applied our method on both synthetic and real data, and it achieves results competitive with or superior to the state-of-the-art in detecting several different types of anomalies.