150 research outputs found
Convex recovery of tensors using nuclear norm penalization
The subdifferential of convex functions of the singular spectrum of real
matrices has been widely studied in matrix analysis, optimization and automatic
control theory. Convex analysis and optimization over spaces of tensors is now
gaining much interest due to its potential applications to signal processing,
statistics and engineering. The goal of this paper is to present an
applications to the problem of low rank tensor recovery based on linear random
measurement by extending the results of Tropp to the tensors setting.Comment: To appear in proceedings LVA/ICA 2015 at Czech Republi
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