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Local Learning Rules for Nonnegative Tucker Decomposition

2009
Analysis of data with high dimensionality in modern applications, such as spectral analysis, neuroscience, chemometrices naturally requires tensorial approaches different from standard matrix factorizations (PCA, ICA, NMF). The Tucker decomposition and its constrained versions with sparsity and/or nonnegativity constraints allow for the extraction of ...
Anh Huy Phan 0001, Andrzej Cichocki
openaire   +1 more source

Sparse Symmetric Format for Tucker Decomposition

IEEE Transactions on Parallel and Distributed Systems, 2023
Shruti Shivakumar   +3 more
openaire   +1 more source

Accurate regularized Tucker decomposition for image restoration

Applied Mathematical Modelling, 2023
Wenwu Gong, Zhejun Huang
exaly  

Fast Recursive Nonnegative Standard and Hierarchical Tucker Decomposition

IEEE Signal Processing Letters, 2019
Krzysztof Fonał, Rafał Zdunek
exaly  

HOID: Higher Order Interpolatory Decomposition for Tensors Based on Tucker Representation

SIAM Journal on Matrix Analysis and Applications, 2016
Arvind K Saibaba
exaly  

Hyperspectral image compression based on lapped transform and Tucker decomposition

Signal Processing: Image Communication, 2015
Lei Wang, Jiaji Wu, Gwanggil Jeon
exaly  

Image Representation and Learning With Graph-Laplacian Tucker Tensor Decomposition

IEEE Transactions on Cybernetics, 2019
Bo Jiang, Chris Ding, Jin Tang
exaly  

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