Math @ Duke

Publications [#236078] of Robert Calderbank
Papers Published
 Chi, Y; Eldar, YC; Calderbank, R, PETRELS: Subspace estimation and tracking from partial observations,
2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp)
(2012),
pp. 33013304, ISSN 15206149 [doi]
(last updated on 2018/10/23)
Abstract: We consider the problem of reconstructing a data stream from a small subset of its entries, where the data stream is assumed to lie in a lowdimensional linear subspace, possibly corrupted by noise. It is also important to track the change of underlying subspace for many applications. This problem can be viewed as a sequential lowrank matrix completion problem in which the subspace is learned in an online fashion. The proposed algorithm, called Parallel Estimation and Tracking by REcursive Least Squares (PETRELS), identifies the underlying lowdimensional subspace via a recursive procedure for each row of the subspace matrix in parallel, and then reconstructs the missing entries via leastsquares estimation if required. PETRELS outperforms previous approaches by discounting observations in order to capture longterm behavior of the data stream and be able to adapt to it. Numerical examples are provided for directionofarrival estimation and matrix completion, comparing PETRELS with state of the art batch algorithms. © 2012 IEEE.


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