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Relevant bibliographies by topics / Higher-order SVD (HOSVD)

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Academic literature on the topic 'Higher-order SVD (HOSVD)'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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Journal articles on the topic "Higher-order SVD (HOSVD)"

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Ahmadi-Asl, Salman, Stanislav Abukhovich, Maame G. Asante-Mensah, et al. "Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD)." IEEE Access 9 (2021): 28684–706. http://dx.doi.org/10.1109/access.2021.3058103.

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Romaszewski, Michał, Piotr Gawron, and Sebastian Opozda. "Dimensionality Reduction of Dynamic Mesh Animations Using HO-SVD." Journal of Artificial Intelligence and Soft Computing Research 3, no. 4 (2013): 277–89. http://dx.doi.org/10.2478/jaiscr-2014-0020.

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Abstract This work presents an analysis of Higher Order Singular Value Decomposition (HOSVD) applied to reduction of dimensionality of 3D mesh animations. Compression error is measured using three metrics (MSE, Hausdorff, MSDM). Results are compared with a method based on Principal Component Analysis (PCA) and presented on a set of animations with typical mesh deformations.

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Ahmadi-Asl, Salman, Stanislav Abukhovich, Maame G. Asante-Mensah, et al. "Corrections to “Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD)”." IEEE Access 12 (2024): 70742. http://dx.doi.org/10.1109/access.2024.3396970.

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Vrabie, Valeriu D., Nicolas Le Bihan, and Jérôme I. Mars. "Multicomponent wave separation using HOSVD/unimodal-ICA subspace method." GEOPHYSICS 71, no. 5 (2006): V133—V143. http://dx.doi.org/10.1190/1.2335387.

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Multicomponent sensor arrays now are commonly used in seismic acquisition to record polarized waves. In this article, we use a three-mode model (polarization mode, distance mode, and temporal mode) to take into account the specific structure of signals that are recorded with these arrays, providing a data-structure-preserving processing. With the suggested model, we propose a multilinear decomposition named higher-order singular value decomposition and unimodal independent component analysis (HOSVD/unimodal ICA) to split the recorded three-mode data into two orthogonal subspaces: the signal an

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Baranyi, Péter. "Extension of the Multi-TP Model Transformation to Functions with Different Numbers of Variables." Complexity 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/8546976.

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The tensor product (TP) model transformation defines and numerically reconstructs the Higher-Order Singular Value Decomposition (HOSVD) of functions. It plays the same role with respect to functions as HOSVD does for tensors (and SVD for matrices). The need for certain advantageous features, such as rank/complexity reduction, trade-offs between complexity and accuracy, and a manipulation power representative of the TP form, has motivated novel concepts in TS fuzzy model based modelling and control. The latest extensions of the TP model transformation, called the multi- and generalised TP model

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Sunil, Kumar Jyothula, and Chandra Prasad Talari Jaya. "An Efficient Transform based Low Rank Tensor Completion to Extreme Visual Recovery." Indian Journal of Science and Technology 15, no. 14 (2022): 608–18. https://doi.org/10.17485/IJST/v15i14.264.

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Abstract <strong>Objective:</strong> To propose an optimization approach in recovering of the corrupted tensors in the high dimensional real time data. <strong>Methods:</strong> The recovering of corrupted tensors is performed by low-rank tensor completion methods. The tensor decomposition methods are used in tensor completion methods. These Tensor decomposition methods; candecomp / parafac (CP), tucker and higher-order Singular Value Decomposition (HoSVD) are used to minimize the rank of a tensor data. The limitations are in finding the rank of a tensor. <strong>Findings:<

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Wang, Yuping, and Junfei Zhang. "A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition." PeerJ Computer Science 8 (March 16, 2022): e923. http://dx.doi.org/10.7717/peerj-cs.923.

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It is a challenging problem to classify multi-dimensional data with complex intrinsic geometry inherent, such as human gesture recognition based on videos. In particular, manifold structure is a good way to characterize intrinsic geometry of multi-dimensional data. The recently proposed sparse coding on Grassmann manifold shows high discriminative power in many visual classification tasks. It represents videos on Grassmann manifold using Singular Value Decomposition (SVD) of the data matrix by vectorizing each image in videos, while vectorization destroys the spatial structure of videos. To ke

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Chiharu, Okuma, Murakami Jun, and Yamamoto Naoki. "Comparison between Higher-Order SVD and Third-order Orthogonal Tensor Product Expansion." March 22, 2009. https://doi.org/10.5281/zenodo.1056250.

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In digital signal processing it is important to approximate multi-dimensional data by the method called rank reduction, in which we reduce the rank of multi-dimensional data from higher to lower. For 2-dimennsional data, singular value decomposition (SVD) is one of the most known rank reduction techniques. Additional, outer product expansion expanded from SVD was proposed and implemented for multi-dimensional data, which has been widely applied to image processing and pattern recognition. However, the multi-dimensional outer product expansion has behavior of great computation complex and has n

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Jiang, Liyuan, Hanbing Chu, Jianjun Yu, et al. "Clutter filtering of angular domain data for contrast-free ultrafast microvascular imaging." Physics in Medicine & Biology, December 2, 2023. http://dx.doi.org/10.1088/1361-6560/ad11a2.

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Abstract Objective. Contrast-free microvascular imaging is clinically valuable for the assessment of physiological status and the early diagnosis of diseases. Effective clutter filtering is essential for microvascular visualization without contrast enhancement. Singular value decomposition (SVD)-based spatiotemporal filter has been widely used to suppress clutter. However, clinical real-time imaging relies on short ensembles (dozens of frames), which limits the implementation of SVD filtering due to the large error of eigen-correlated estimations and high dependence on optimal threshold when u

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Chiharu, Okuma, Yamamoto Naoki, and Murakami Jun. "An Improved Algorithm for Calculation of the Third-order Orthogonal Tensor Product Expansion by Using Singular Value Decomposition." February 27, 2010. https://doi.org/10.5281/zenodo.1084169.

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As a method of expanding a higher-order tensor data to tensor products of vectors we have proposed the Third-order Orthogonal Tensor Product Expansion (3OTPE) that did similar expansion as Higher-Order Singular Value Decomposition (HOSVD). In this paper we provide a computation algorithm to improve our previous method, in which SVD is applied to the matrix that constituted by the contraction of original tensor data and one of the expansion vector obtained. The residual of the improved method is smaller than the previous method, truncating the expanding tensor products to the same number of ter

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Conference papers on the topic "Higher-order SVD (HOSVD)"

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Pereira, Pedro M., Bruno S. Ferreira, and Fernando P. Bernardo. "Multi-Dimensional Singular Value Decomposition of Scale-Varying CFD Data: Analyzing Scale-Up Effects in Fermentation Processes." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.129601.

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The scale-up of processes with complex fluid flow presents significant challenges in process engineering, particularly in fermentation. Computational fluid dynamics (CFD) is a crucial tool for accurately modelling the hydrodynamic environment in bioreactors and understanding the effects of scale-up. This study utilizes Higher Order SVD (HOSVD), which is the multidimensional extension of Singular Value Decomposition (SVD), to identify the dominant structures (modes) of fluid flow in CFD data of fermentation process simulations. Similarly to Proper Orthogonal Decomposition (POD), also based on S

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Afra, Sardar, Eduardo Gildin, and Mohammadali Tarrahi. "Heterogeneous reservoir characterization using efficient parameterization through higher order SVD (HOSVD)." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859246.

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