Berkant Savas
Berkant Savas
Department of Science and Technology, Linköping University
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Geciteerd door
Geciteerd door
Handwritten digit classification using higher order singular value decomposition
B Savas, L Eldén
Pattern recognition 40 (3), 993-1003, 2007
A Newton–Grassmann Method for Computing the Best Multilinear Rank- Approximation of a Tensor
L Eldén, B Savas
SIAM Journal on Matrix Analysis and applications 31 (2), 248-271, 2009
Quasi-Newton methods on Grassmannians and multilinear approximations of tensors
B Savas, LH Lim
SIAM Journal on Scientific Computing 32 (6), 3352-3393, 2010
Supervised link prediction using multiple sources
Z Lu, B Savas, W Tang, IS Dhillon
2010 IEEE international conference on data mining, 923-928, 2010
Clustered low rank approximation of graphs in information science applications
B Savas, IS Dhillon
Proceedings of the 2011 SIAM International Conference on Data Mining, 164-175, 2011
Krylov-type methods for tensor computations I
B Savas, L Eldén
Linear Algebra and its Applications 438 (2), 891-918, 2013
Analyses and tests of handwritten digit recognition algorithms
B Savas
LiTH-MAT-EX-2003-01, Linkˆping University, Department of Mathematics, 2003
Scalable affiliation recommendation using auxiliary networks
V Vasuki, N Natarajan, Z Lu, B Savas, I Dhillon
ACM Transactions on Intelligent Systems and Technology (TIST) 3 (1), 1-20, 2011
Clustered embedding of massive social networks
HH Song, B Savas, TW Cho, V Dave, Z Lu, IS Dhillon, Y Zhang, L Qiu
ACM SIGMETRICS Performance Evaluation Review 40 (1), 331-342, 2012
Parallel clustered low-rank approximation of graphs and its application to link prediction
X Sui, TH Lee, JJ Whang, B Savas, S Jain, K Pingali, I Dhillon
International Workshop on Languages and Compilers for Parallel Computing, 76-95, 2012
Rank reduction and volume minimization approach to state-space subspace system identification
B Savas, D Lindgren
Signal processing 86 (11), 3275-3285, 2006
Perturbation theory and optimality conditions for the best multilinear rank approximation of a tensor
L Eldén, B Savas
SIAM journal on matrix analysis and applications 32 (4), 1422-1450, 2011
Algorithms in data mining using matrix and tensor methods
B Savas
Matematiska institutionen, 2008
Clustered matrix approximation
B Savas, IS Dhillon
SIAM Journal on Matrix Analysis and Applications 37 (4), 1531-1555, 2016
The maximum likelihood estimate in reduced‐rank regression
L Eldén, B Savas
Numerical linear algebra with applications 12 (8), 731-741, 2005
Dimensionality reduction and volume minimization—generalization of the determinant minimization criterion for reduced rank regression problems
B Savas
Linear algebra and its applications 418 (1), 201-214, 2006
Big Data
D Zhang, Y Lin, Z Song, I Dhillon, H Yu, C Hsieh, Q Lei, I Yen, C Wu, ...
IEEE Computer (IEEE Computer) 49 (4), 52-60, 2016
Algorithm Package Manual: Best Low Rank Tensor Approximation
B Savas
Department of Mathematics, Linköping Univeristy, Linköping, Sweden, 2008
Social Network Analysis: Fast and Memory-Efficient Low-Rank Approximation of Massive Graphs
I Dhillon, B Savas, Y Zhang
Householder Symposium XVIII on Numerical Linear Algebra, 55, 2011
Toolbox for Grassmann Manifold Computations
B Savas
Department of Mathematics, Linköping Univeristy, Linköping, Sweden, 2008
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