The N-way Toolbox for MATLAB

By Claus A. Andersson, Rasmus Bro. 2000

1,224 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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Citations in the corpus

A literature survey of low‐rank tensor approximation techniques (2013, neutral): “There are several M ATLAB toolboxes available for dealing with tensors in CP and Tucker decomposition, including the Tensor Toolbox [12, 13], the N -way toolbox [7], the PLS Toolbox [267], and the Tensor-lab [240].”
Modeling multi‐way data with linearly dependent loadings (2009, methodology): “To run the PARALIND algorithm it is advisable to have the N-way toolbox [37] which is available at the same site.”
GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries (2012, methodology): “In [4], Bader et al. introduce a way to alleviate the above problem, when the tensor is represented in a sparse form, in Matlab.”
On the best low multilinear rank approximation of higher-order tensors (2010, methodology): “Such a preprocessing step is implemented for example in the N -way toolbox for MATLAB [ 6 ].”
missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016, methodology): “Some are available as MATLAB toolboxes (Andersson and Bro 2000) or as standalone software products (Kroonenberg 2011).”
CANDECOMP/PARAFAC Decomposition of High-Order Tensors Through Tensor Reshaping (2012, methodology): “ALS [17] factorized both and into rank-1 tensors in 1000 iterations, and stopped when .”
Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations (2009, methodology): “For tensor factorization, the results were compared with some existing algorithms: the NMWF [31], the lsNTF [32] and also with two e fficient implementations of general form of PARAFAC ALS algorithm by Kolda and Bader [16] (denoted as ALS K) and by Andersson and Bro [33] (denoted as ALSB).”
Accelerated Canonical Polyadic Decomposition Using Mode Reduction (2012, neutral): “All the experiments were done in MATLAB 2008a on a computer with Intel i7 3.33GHz CPU and 24GB memory running Windows 7.”
Tensor Decompositions and Applications (2009, background): “In section 3, we discuss the CP decomposition, its connection to tensor rank and tensor border rank, conditions for uniqueness, algorithms and computational issues, and applications.”
A comparison of algorithms for fitting the PARAFAC model (2006, methodology): “DTLD and PARAFAC-ALS are part of the N-Way toolbox (Andersson and Bro, 2000).”
A review on machine learning principles for multi-view biological data integration (2016, neutral): “N-way Toolbox [169] Multi-way PARAFAC, PLS, and”
Scalable Tensor Factorizations for Incomplete Data (2010, background): “10 [32]) and INDAFAC [33], which is a dampedGauss–Newtonmethod proposed by Tomasi and Bro [7].”