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On The Practicality of Subspace Tracking in Information Systems
Modeling and characterizing information systems' observation data (i.e. logs) is fundamental for proper system configuration, security analysis, and assessing system health. Due to the underlying dynamics of such systems, observations can be viewed as highdimensional, time varying, multivariate data. One broad class for concisely modeling systems with such data points is lowrank modeling where the observations manifest themselves in a lower-dimensional subspace. Subspace Tracking plays an important role in many applications, such as signal processing, image tracking and recognition, and machine learning. However, it is not well understood which tracker is suitable for a given information system in a practical setting. In this paper, we present a comprehensive comparative analysis of three state-of-the-art, low rank modeling approaches; GROUSE, PETRELS, and RankMin . These algorithms will be compared in terms of their convergence and stability, parameter sensitivity, and robustness in dealing with missing data for synthetic and real information systems' data sets; we will then summarize our findings.