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Robust Matrix Completion via Alternating Projection

Abstract: 

Matrix completion aims to find the missing entries from incomplete observations using the low-rank property. Conventional convex optimization based techniques minimize the nuclear norm subject to a constraint on the Frobenius norm of the residual. However, they are not robust to outliers and have a high computational complexity. Different from the existing schemes based on solving a minimization problem, we formulate matrix completion as a feasibility problem. An alternating projection algorithm is then devised to find a feasible point in the intersection of the low-rank constraint set, and the lp-norm constraint set for handling outlier observations. This presentation is a companion work of: X. Jiang, Z. Zhong, X. Liu and H. C. So, "Robust matrix completion via alternating projection," IEEE Signal Processing Letters, vol. 24, no. 5, pp. 579-583, May 2017

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19 June 2017 - 11:39pm
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[1] , "Robust Matrix Completion via Alternating Projection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1798. Accessed: Jul. 23, 2017.
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title = {Robust Matrix Completion via Alternating Projection},
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T1 - Robust Matrix Completion via Alternating Projection
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. (2017). Robust Matrix Completion via Alternating Projection. IEEE SigPort. http://sigport.org/1798
, 2017. Robust Matrix Completion via Alternating Projection. Available at: http://sigport.org/1798.
. (2017). "Robust Matrix Completion via Alternating Projection." Web.
1. . Robust Matrix Completion via Alternating Projection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1798