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Change-point detection is of great interest in applications such as target tracking, anomaly detection and trend filtering. In many cases, it is also desirable to localize the change- point, if it exists. Motivated by the unprecedented scale and rate of modern high-dimensional streaming data, we propose a change-point detection and estimation procedure based on data sketching, which only requires...
Estimating and tracking the covariance matrix of high-dimensional data streams with low complexities in acquisition, storage and computation are of great interest in modern data-intensive applications. This paper develops an online covariance estimation and tracking algorithm for a recently developed covariance sketching framework that requires a single sketch per sample [1], by leveraging the low-rank...
This paper proposes a simple sensing and estimation framework, called one-bit sketching, to faithfully recover the principal subspace of a data stream or dataset from a set of one-bit measurements collected at distributed sensors. Each bit indicates the comparison outcome between energy projections of the local sample covariance matrix over a pair of random directions. By leveraging low-dimensional...
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