Decomposition-by-normalization (DBN): leveraging approximate functional dependencies for efficient CP and Tucker decompositions

DOI10.1007/s10618-015-0401-6zbMath1409.68236OpenAlexW2089441007MaRDI QIDQ1741131

K. Selçuk Candan, Mi Jung Kim

Publication date: 3 May 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-015-0401-6

zbMATH Keywords

tensor decompositionTucker decompositionCP decompositionparallel CP decompositionparallel tensor decompositionparallel Tucker decomposition

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Database theory (68P15) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Mining relaxed functional dependencies from data

Uses Software

• FastFDs
• TANE
• UCI-ml
• TensorToolbox
• N-way Toolbox
• MovieLens
• ArnetMiner
• MultiVis

Cites Work

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• Tensor Decompositions and Applications
• Hierarchical multilinear models for multiway data
• On the complexity of inferring functional dependencies
• 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
• Efficient MATLAB Computations with Sparse and Factored Tensors
• Tane: An Efficient Algorithm for Discovering Functional and Approximate Dependencies
• A simple min-cut algorithm
• Tensor-CUR Decompositions for Tensor-Based Data

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