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Berkant Savas
Researcher at Linköping University
Publications - 23
Citations - 1071
Berkant Savas is an academic researcher from Linköping University. The author has contributed to research in topics: Rank (linear algebra) & Multilinear map. The author has an hindex of 14, co-authored 23 publications receiving 1001 citations. Previous affiliations of Berkant Savas include University of Texas at Austin.
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Algorithms in data mining using matrix and tensor methods
TL;DR: In many fields of science, engineering, and economics large amounts of data are stored and there is a need to analyze these data in order to extract information for various purposes.
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Krylov-Type Methods for Tensor Computations
Berkant Savas,Lars Eldén +1 more
TL;DR: The tensor Krylov methods are intended for the computation of low multilinear rank approximations of large and sparse tensors, but they are also useful for certain dense and structured tensors for computing their higher order singular value decompositions or obtaining starting points for the best low-rank computations of tensors.
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Clustered Matrix Approximation
TL;DR: A probabilistic approach that uses randomness to compute a clustered matrix approximation framework that is considerably more accurate with less memory usage than truncated SVD approximations, which are optimal with respect to rank.
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The Maximum Likelihood Estimate in Reduced-Rank Regression
Lars Eldén,Berkant Savas +1 more
TL;DR: In previous work by Stoica and Viberg the reduced-rank regression problem is solved in a maximum likelihood sense, and an alternative numerical procedure is proposed to solve the problem.
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Rank reduction and volume minimization approach to state-space subspace system identification
Berkant Savas,David Lindgren +1 more
TL;DR: The determinant criterion is, due to potential rank-deficiencies, not general enough to handle all problem instances and a more general minimization criterion is considered, rank reduction followed by volume minimization.