Results 11 to 20 of about 3,363 (257)
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 0001
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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.
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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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Tucker Tensor Decomposition on FPGA [PDF]
2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019 Tensor computation has emerged as a powerful mathematical tool for solving high-dimensional and/or extreme-scale problems in science and engineering. The last decade has witnessed tremendous advancement of tensor computation and its applications in machine learning and big data. However, its hardware optimization on resource-constrained devices remains
Kaiqi Zhang 0002 +2 more
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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.
Dimitris G. Chachlakis +3 more
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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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A Hybrid Norm for Guaranteed Tensor Recovery
Frontiers in Physics, 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 and
Yihao Luo +5 more
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Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition
Applied Sciences, 2022 For the purpose of solving the large temperature field reconstruction error caused by different measuring point arrangements and the problem that the prior dataset cannot be built due to data loss or distortion in actual measurement, a three-dimensional ...
Zhaoyu Liu +3 more
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