Search results for "hierarchical alternating least squares"

showing 2 items of 2 documents

Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition

2019

Real-world data exhibiting high order/dimensionality and various couplings are linked to each other since they share some common characteristics. Coupled tensor decomposition has become a popular technique for group analysis in recent years, especially for simultaneous analysis of multi-block tensor data with common information. To address the multiblock tensor data, we propose a fast double-coupled nonnegative Canonical Polyadic Decomposition (FDC-NCPD) algorithm in this study, based on the linked CP tensor decomposition (LCPTD) model and fast Hierarchical Alternating Least Squares (Fast-HALS) algorithm. The proposed FDCNCPD algorithm enables simultaneous extraction of common components, i…

Computer sciencelinked CP tensor decomposition (LCPTD)02 engineering and technologySignal-to-noise ratiotensor decompositionConvergence (routing)0202 electrical engineering electronic engineering information engineeringDecomposition (computer science)TensorHigh orderta113konvergenssiconvergencesignal to noise ratio020206 networking & telecommunicationsbrain modelinghierarchical alternating least squares (HALS)Alternating least squaresCore (graph theory)coupled tensor decomposition020201 artificial intelligence & image processingAlgorithmsignal processing algorithmselectroencephalographymathematical modelCurse of dimensionality
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Generalization of Linked Canonical Polyadic Tensor Decomposition for Group Analysis

2019

Real-world data are often linked with each other since they share some common characteristics. The mutual linking can be seen as a core driving force of group analysis. This study proposes a generalized linked canonical polyadic tensor decomposition (GLCPTD) model that is well suited to exploiting the linking nature in multi-block tensor analysis. To address GLCPTD model, an efficient algorithm based on hierarchical alternating least squa res (HALS) method is proposed, termed as GLCPTD-HALS algorithm. The proposed algorithm enables the simultaneous extraction of common components, individual components and core tensors from tensor blocks. Simulation experiments of synthetic EEG data analysi…

canonical polyadicComputer scienceGeneralizationNoise reductionlinked tensor decomposition020206 networking & telecommunications02 engineering and technologyIterative reconstructionhierarchical alternating least squares03 medical and health sciencessimultaneous extraction0302 clinical medicineGroup analysisCore (graph theory)0202 electrical engineering electronic engineering information engineeringTensor decompositionTensorAlgorithmRealization (systems)030217 neurology & neurosurgery
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