Abstract
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 [6] to the tensors setting.
Cite
CITATION STYLE
Chrétien, S., & Wei, T. (2015). Convex recovery of tensors using nuclear norm Penalization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9237, pp. 360–367). Springer Verlag. https://doi.org/10.1007/978-3-319-22482-4_42
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