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Rank-Adaptive Tensor Completion Based on Tucker Decomposition [PDF]
Entropy, 2023 Tensor completion is a fundamental tool to estimate unknown information from observed data, which is widely used in many areas, including image and video recovery, traffic data completion and the multi-input multi-output problems in information theory ...
Siqi Liu, Xiaoyu Shi, Qifeng Liao
doaj +4 more sources
L1-Norm Tucker Tensor Decomposition [PDF]
IEEE Access, 2019 Tucker decomposition is a standard multi-way generalization of Principal-Component Analysis (PCA), appropriate for processing tensor data. Similar to PCA, Tucker decomposition has been shown to be sensitive against faulty data, due to its L2-norm-based ...
Dimitris G. Chachlakis +2 more
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Nonconvex Nonlocal Tucker Decomposition for 3D Medical Image Super-Resolution [PDF]
Frontiers in Neuroinformatics, 2022 Limited by hardware conditions, imaging devices, transmission efficiency, and other factors, high-resolution (HR) images cannot be obtained directly in clinical settings.
Huidi Jia +11 more
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Multimodal Tucker Decomposition for Gated RBM Inference [PDF]
Applied Sciences, 2021 Gated networks are networks that contain gating connections in which the output of at least two neurons are multiplied. The basic idea of a gated restricted Boltzmann machine (RBM) model is to use the binary hidden units to learn the conditional ...
Mauricio Maldonado-Chan +2 more
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VOLUME-REGULARIZED NONNEGATIVE TUCKER DECOMPOSITION WITH IDENTIFIABILITY GUARANTEES. [PDF]
Proc IEEE Int Conf Acoust Speech Signal Process, 2023 It is well-known that the Tucker decomposition of a multi-dimensional tensor is not unique, because its factors are subject to rotation ambiguities similar to matrix factorization models. Inspired by the recent success in the identifiability of nonnegative matrix factorization, the goal of this work is to achieve similar results for nonnegative Tucker ...
Sun Y, Huang K.
europepmc +3 more sources
Muscle Synergy during Wrist Movements Based on Non-Negative Tucker Decomposition [PDF]
SensorsModular control of the muscle, which is called muscle synergy, simplifies control of the movement by the central nervous system. The purpose of this study was to explore the synergy in both the frequency and movement domains based on the non-negative ...
Xiaoling Chen +5 more
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Functional Connectome Fingerprinting Through Tucker Tensor Decomposition
Applied SciencesThe human functional connectome (FC) is a representation of the functional couplings between brain regions derived from blood oxygen level-dependent (BOLD) signals.
Vitor Carvalho +4 more
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Efficient enhancement of low-rank tensor completion via thin QR decomposition [PDF]
Frontiers in Big DataLow-rank tensor completion (LRTC), which aims to complete missing entries from tensors with partially observed terms by utilizing the low-rank structure of tensors, has been widely used in various real-world issues.
Yan Wu, Yunzhi Jin
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Image Clustering Algorithm Based on Hypergraph Regularized Nonnegative Tucker Decomposition [PDF]
Jisuanji gongcheng, 2022 The internal geometry structure of high-dimensional data is ignored when nonnegative tensor decomposition is applied to image clustering.To solve this problem, we propose a Hypergraph regularized Nonnegative Tucker Decomposition(HGNTD) model by adding a ...
CHEN Luyao, LIU Qilong, XU Yunxia, CHEN Zhen
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Orthogonal Nonnegative Tucker Decomposition [PDF]
SIAM Journal on Scientific Computing, 2021 In this paper, we study the nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relaxation algorithm of the augmented Lagrangian function to solve the optimization problem. The convergence of the algorithm is given.
Junjun Pan +4 more
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