Results 21 to 30 of about 1,077,071 (292)
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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Optimization landscape of Tucker decomposition [PDF]
Mathematical Programming, 2020 Tucker decomposition is a popular technique for many data analysis and machine learning applications. Finding a Tucker decomposition is a nonconvex optimization problem. As the scale of the problems increases, local search algorithms such as stochastic gradient descent have become popular in practice.
Abraham Frandsen, Rong Ge
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Design and Implementation of Tucker Decomposition Module Based on CUDA and CUBLAS [PDF]
Jisuanji gongcheng, 2019 Because tensor Tucker decomposition is widely used in image processing,face recognition,signal processing and other fields,Tucker decomposition algorithm becomes a key research object.However,the current popular Tucker decomposition algorithm needs to ...
ZHOU Qi,CHAI Xiaoli,MA Kejie,YU Zeren
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Dynamic L1-Norm Tucker Tensor Decomposition [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2020 <p>Tucker decomposition is a standard method for processing multi-way (tensor) measurements and finds many applications in machine learning and data mining, among other fields. When tensor measurements arrive in a streaming fashion or are too many to jointly decompose, incremental Tucker analysis is preferred.
Panos P. Markopoulos +3 more
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Robust Barron-Loss Tucker Tensor Decomposition [PDF]
2021 55th Asilomar Conference on Signals, Systems, and Computers, 2021 <p>In this work, we propose a new formulation for low-rank tensor approximation, with tunable outlier-robustness, and present a unified algorithmic solution framework. This formulation relies on a new generalized robust loss function (Barron loss), which encompasses several well-known loss-functions with variable outlier resistance.
Panos P. Markopoulos, Mahsa Mozaffari
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Tensor decomposition based networks for nuclei segmentation and classification
Electronics Letters, 2022 Nuclei segmentation and classification for Haematoxylin & Eosin stained histology images is a challenging task because of many issues, large intra‐class variability among nuclei, overlapping nuclei etc.
Jinhao Chen, Zhao Chen
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Discriminative Nonnegative Tucker Decomposition for Tensor Data Representation
Mathematics, 2022 Nonnegative Tucker decomposition (NTD) is an unsupervised method and has been extended in many applied fields. However, NTD does not make use of the label information of sample data, even though such label information is available.
Wenjing Jing, Linzhang Lu, Qilong Liu
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Orthogonal tucker decomposition using factor priors for 2D+3D facial expression recognition
IET Biometrics, 2021 In this article, an effective approach is proposed to recognise the 2D+3D facial expression automatically based on orthogonal Tucker decomposition using factor priors (OTDFPFER).
Yunfang Fu +4 more
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Application of Tucker Decomposition in Temperature Distribution Reconstruction
Applied Sciences, 2022 Constrained by cost, measuring conditions and excessive calculation, it is difficult to reconstruct a 3D real-time temperature field. For the purpose of solving these problems, a three-dimensional temperature distribution reconstruction algorithm based ...
Zhaoyu Liu +4 more
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Orthogonal random projection for tensor completion
IET Computer Vision, 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 (SVD ...
Yali Feng, Guoxu Zhou
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