Results 1 to 10 of about 1,076,972 (193)
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
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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.
Sun Y, Huang K.
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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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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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Tucker decomposition-based temporal knowledge graph completion [PDF]
Knowledge-Based Systems, 2022 Knowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the knowledge graphs. To enrich the existing knowledge graphs, recent years witness that many algorithms for link prediction and knowledge graphs embedding have been designed to ...
Jian-Hua Tao
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Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition. [PDF]
BioinformaticsSpatial transcripome (ST) profiling can reveal cells’ structural organizations and functional roles in tissues. However, deciphering the spatial context of gene expressions in ST data is a challenge—the high-order structure hiding in whole transcriptome ...
Broadbent C, Song T, Kuang R.
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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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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingDeep learning methodologies have demonstrated considerable effectiveness in hyperspectral anomaly detection (HAD). However, the practicality of deep learning-based HAD in real-world applications is impeded by challenges arising from limited labeled data,
Yulei Wang +4 more
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Remote SensingHyperspectral image (HSI) denoising is an important preprocessing step for downstream applications. Fully characterizing the spatial-spectral priors of HSI is crucial for denoising tasks.
Cheng Cheng +4 more
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