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Akademický článek

Online Privacy-Preserving EEG Classification by Source-Free Transfer Learning

Autor: Hanrui Wu, Zhengyan Ma, Zhenpeng Guo, Yanxin Wu, Jia Zhang, Guoxu Zhou, Jinyi Long

Publikováno v: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3059-3070 (2024)

Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learning to assist the learning task in the new subject, i.e., target domain, by leveraging beneficial inf

Externí odkaz: https://doaj.org/article/075011f69eef4f6cbc6ac94ab6ef1c5a

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Akademický článek

Contour information regularized tensor ring completion for realistic image restoration

Autor: Zhi Yu, Yihao Luo, Zhifa Liu, Guoxu Zhou

Publikováno v: IET Image Processing, Vol 16, Iss 13, Pp 3499-3506 (2022)

Abstract Tensor completion has gained considerable research interest in recent years and has been frequently applied to image restoration. This type of method basically employs the low‐rank nature of images, implicitly requiring that the whole pict

Externí odkaz: https://doaj.org/article/0470612df5e84e18bf86624d2eeb358e

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Akademický článek

A Hybrid Norm for Guaranteed Tensor Recovery

Autor: Yihao Luo, Andong Wang, Guoxu Zhou, Qibin Zhao

Publikováno v: Frontiers in Physics, Vol 10 (2022)

Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating low-rankness in the spectral domain over other tensor decompositions (like Tucker decomposition), t-SVD-based tensor learning has shown promising performance

Externí odkaz: https://doaj.org/article/897ece97cd8e459b9c142c991d8f9b95

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Akademický článek

Orthogonal random projection for tensor completion

Autor: Yali Feng, Guoxu Zhou

Publikováno v: IET Computer Vision, Vol 14, Iss 5, Pp 233-240 (2020)

The low‐rank tensor completion problem, which aims to recover the missing data from partially observable data. However, most of the existing tensor completion algorithms based on Tucker decomposition cannot avoid using singular value decomposition

Externí odkaz: https://doaj.org/article/64334701555d46e88369fb52d887b142

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Akademický článek

Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization

Autor: Jinshi Yu, Guoxu Zhou, Andrzej Cichocki, Shengli Xie

Publikováno v: IEEE Access, Vol 6, Pp 58096-58105 (2018)

Nonsmooth nonnegative matrix factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfactory fit to data. However, nsNMF as well as other existing NMF methods

Externí odkaz: https://doaj.org/article/3c83f64f44164b50a2c3722bc02d56a0

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Akademický článek

Guaranteed Robust Tensor Completion via ∗L-SVD with Applications to Remote Sensing Data

Autor: Andong Wang, Guoxu Zhou, Qibin Zhao

Publikováno v: Remote Sensing, Vol 13, Iss 18, p 3671 (2021)

This paper conducts a rigorous analysis for the problem of robust tensor completion, which aims at recovering an unknown three-way tensor from incomplete observations corrupted by gross sparse outliers and small dense noises simultaneously due to var

Externí odkaz: https://doaj.org/article/b7282ba0c29641cf97fc3fdb766f8ec5

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Robust to Rank Selection: Low-Rank Sparse Tensor-Ring Completion

Autor: Weijun Sun, Jinshi Yu, Shengli Xie, Guoxu Zhou

Publikováno v: IEEE Transactions on Neural Networks and Learning Systems. 34:2451-2465

Tensor-ring (TR) decomposition was recently studied and applied for low-rank tensor completion due to its powerful representation ability of high-order tensors. However, most of the existing TR-based methods tend to suffer from deterioration when the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b4affd80148935032a62eb8d1803a58
https://doi.org/10.1109/tnnls.2021.3106654

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