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HOTTBOX is a Python library for exploratory analysis and visualisation of multi-dimensional arrays of data, also known as tensors.
“PARAFAC2 Part I: A direct fitting algorithm for the PARAFAC2 model,”
H. A. L. Kiers, J. M. F. Ten Berge, and R. Bro, · 1999
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“A multilinear singular value decomposition,”
L. De Lathauwer, B. De Moor, and J. Vandewalle, · 2000
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“On the best rank-1 and rank- ( r 1 , r 2 , … , r n ) (r_{1},r_{2},...,r_{n}) approximation of higher-order tensors,”
L. De Lathauwer, B. De Moor, and J. Vandewalle, · 2000
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“Multilinear analysis of image ensembles: Tensorfaces,”
M. A. O. Vasilescu and D. Terzopoulos, · 2002
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L. De Lathauwer, · 2006
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“Cross-language information retrieval using PARAFAC2,”
P. A. Chew, B. W. Bader, T. G. Kolda, and A. Abdelali, · 2007
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“Decompositions of a higher-order tensor in block terms. Part II: Definitions and uniqueness,”
L. De Lathauwer, · 2008
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“Multi-mode tensor representation of motion data,”
B. Krüger, J. Tautges, M. Müller, and A. Weber, · 2008
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“Tensor decompositions and applications,”
T. G. Kolda and B. W. Bader, · 2009
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“Applications of tensor (multiway array) factorizations and decompositions in data mining,”
M. Mørup, · 2011
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“Tensor-train decomposition,”
I. Oseledets, · 2011
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“Optimization-based algorithms for tensor decompositions: Canonical polyadic decomposition, decomposition in rank- ( L r , L r , 1 ) (L_{r},L_{r},1) terms, and a new generalization,”
L. Sorber, M. Van Barel, and L. De Lathauwer, · 2013
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“Scalable and compact representation for motion capture data using tensor decomposition,”
J. Hou, L. Chau, N. Magnenat-Thalmann, and Y. He, · 2014
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“Tensor decompositions for signal processing applications: From two-way to multiway component analysis,”
A. Cichocki, D. P. Mandic, L. De Lathauwer, G. Zhou, Q. Zhao, C. Caiafa, and H. A. Phan, · 2015
Cited alongside, same era.
“Tensor decomposition of EEG signals: a brief review,”
F. Cong, Q. Lin, L. Kuang, X. Gong, P. Astikainen, and T. Ristaniemi, · 2015
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“Online stochastic tensor decomposition for background subtraction in multispectral video sequences,”
S. Andrews, J. Sajid, K. J. Soon, B. Thierry, and Z. El-hadi, · 2015
Cited alongside, same era.
“Extraction of common task signals and spatial maps from group fMRI using a PARAFAC-based tensor decomposition technique,”
B. Sen and K. K. Parhi, · 2017
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“Extracting multi-mode ERP features using fifth-order nonnegative tensor decomposition,”
Deqing Wang, Yongjie Zhu, Tapani Ristaniemi, and Fengyu Cong, · 2018
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“TensorD: A tensor decomposition library in TensorFlow,”
L. Hao, S. Liang, J. Ye, and Z. Xu, · 2018
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“Refreshing DSP courses through biopresence in the curriculum: A successful paradigm,”
A. Moniri, I. Kisil, A. G. Constantinides, and D. P. Mandic, · 2018
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“Tensor Train decomposition on TensorFlow (T3F),”
A. Novikov, P. Izmailov, V. Khrulkov, M. Figurnov, and I. Oseledets, · 2018
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“Tensor networks for dimensionality reduction and large-scale optimization. Part 1: Low-rank tensor decompositions,”
A. Cichocki, N. Lee, I. Oseledets, A. H. Phan, Q. Zhao, and D. P. Mandic, · 2016
Cited alongside, same era.
“Tensorlab 3.0 – Numerical optimization strategies for large-scale constrained and coupled matrix/tensor factorization,”
N. Vervliet, O. Debals, and L. De Lathauwer, · 2016
Cited alongside, same era.
“Linked component analysis from matrices to high-order tensors: Applications to biomedical data,”
G. Zhou, Q. Zhao, Y. Zhang, T. Adalı, S. Xie, and A. Cichocki, · 2016
Cited alongside, same era.
“Scout: Scalable coupled matrix-tensor factorization-algorithm and discoveries,”
B. Jeon, I. Jeon, L. Sael, and U. Kang, · 2016
Cited alongside, same era.
“Tensor networks for dimensionality reduction and large-scale optimization. Part 2: Applications and future perspectives,”
A. Cichocki, A. H. Phan, Q. Zhao, N. Lee, I. Oseledets, M. Sugiyama, and D. P. Mandic, · 2017
Cited alongside, same era.
“MATLAB Tensor Toolbox Version 3.1,”
B. W. Bader, T. G. Kolda, and et al
Cited in the paper.
“No reference stereo video quality assessment based on motion feature in tensor decomposition domain,”
G. Jiang, S. Liu, M. Yu, F. Shao, Z. Peng, and F. Chen, · 2018
Later among the works it cites.
“A practical randomized CP tensor decomposition,”
C. Battaglino, G. Ballard, and T. G. Kolda, · 2018
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“Tensor ensemble learning for multidimensional data,”
I. Kisil, A. Moniri, and D. P. Mandic, · 2018
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“Tensorly: Tensor learning in Python,”
J. Kossaifi, Y. Panagakis, A. Anandkumar, and M. Pantic, · 2019
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“A statistically identifiable model for tensor-valued Gaussian random variables,”
B. Scalzo Dees and D. P. Mandic, · 2019
Later among the works it cites.