Semidefinite Relaxations for Best Rank-1 Tensor Approximations
نویسندگان
چکیده
منابع مشابه
Best subspace tensor approximations
In many applications such as data compression, imaging or genomic data analysis, it is important to approximate a given tensor by a tensor that is sparsely representable. For matrices, i.e. 2-tensors, such a representation can be obtained via the singular value decomposition which allows to compute the best rank k approximations. For t-tensors with t > 2 many generalizations of the singular val...
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Article history: Received 31 October 2011 Accepted 15 May 2012 Available online 28 June 2012 Submitted by Volker Mehrmann AMS classification: 15A60 15B48 15A03
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ژورنال
عنوان ژورنال: SIAM Journal on Matrix Analysis and Applications
سال: 2014
ISSN: 0895-4798,1095-7162
DOI: 10.1137/130935112