Algorithms for Sparse Nonnegative Tucker Decompositions

DOI10.1162/neco.2008.11-06-407zbMath1178.68447OpenAlexW2108837778WikidataQ28424414 ScholiaQ28424414MaRDI QIDQ3519229

Morten Mørup, Sidse Marie Arnfred, Lars Kai Hansen

Publication date: 13 August 2008

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco.2008.11-06-407

zbMATH Keywords

Tucker modelPARAFAC modellarge-scale multiway data

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (11)

Non-negative matrix factorization algorithm for the deconvolution of one dimensional chromatograms ⋮ Multiplicative algorithms for symmetric nonnegative tensor factorizations and its applications ⋮ Nonnegative tensor factorization as an alternative Csiszar-Tusnady procedure: algorithms, convergence, probabilistic interpretations and novel probabilistic tensor latent variable analysis algorithms ⋮ Non-negative low-rank approximations for multi-dimensional arrays on statistical manifold ⋮ Sparse non-negative tensor factorization using columnwise coordinate descent ⋮ A proximal ANLS algorithm for nonnegative tensor factorization with a periodic enhanced line search. ⋮ Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing ⋮ Nonnegative Tensor Patch Dictionary Approaches for Image Compression and Deblurring Applications ⋮ An algorithm for overlapped chromatogram separation ⋮ A scalable estimator of sets of integral operators ⋮ Alternating proximal gradient method for sparse nonnegative Tucker decomposition

Uses Software

• N-way Toolbox

Cites Work

• Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics
• Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
• Positive tensor factorization
• Analysis of Discrete Ill-Posed Problems by Means of the L-Curve
• A Multilinear Singular Value Decomposition
• Learning the parts of objects by non-negative matrix factorization
• Projected Gradient Methods for Nonnegative Matrix Factorization
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution

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