The Computation of Low Multilinear Rank Approximations of Tensors via Power Scheme and Random Projection

2020 ◽  
Vol 41 (2) ◽  
pp. 605-636
Author(s):  
Maolin Che ◽  
Yimin Wei ◽  
Hong Yan
Keyword(s):  
Random Projection ◽  
Multilinear Rank

scholarly journals Applying a random projection algorithm to optimize machine learning model for breast lesion classification

2021 ◽  
pp. 1-1
Author(s):  
Morteza Heidari ◽  
Sivaramakrishnan Lakshmivarahan ◽  
Seyedehnafiseh Mirniaharikandehei ◽  
Gopichandh Danala ◽  
Sai Kiran R. Maryada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Lesion ◽  
Random Projection ◽  
Learning Model ◽  
Projection Algorithm ◽  
Machine Learning Model ◽  
Lesion Classification

Efficient sparse unmixing analysis for hyperspectral imagery based on random projection

2012 ◽  
Vol 23 (7-8) ◽  
pp. 2281-2293
Author(s):  
Zhenwei Shi ◽  
Liu Liu ◽  
Xinya Zhai ◽  
Zhiguo Jiang
Keyword(s):  
Hyperspectral Imagery ◽  
Random Projection

Improved $${{\ell }^{1}}$$ ℓ 1 -tracker using robust PCA and random projection

2016 ◽  
Vol 27 (4) ◽  
pp. 577-583
Author(s):  
Dongjing Shan ◽  
Zhang Chao
Keyword(s):  
Random Projection ◽  
Robust Pca

scholarly journals Distributed Constrained Stochastic Subgradient Algorithms Based on Random Projection and Asynchronous Broadcast over Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Junlong Zhu ◽  
Ping Xie ◽  
Qingtao Wu ◽  
Mingchuan Zhang ◽  
Ruijuan Zheng ◽  
...  
Keyword(s):  
Information Exchange ◽  
Optimal Solution ◽  
Random Projection ◽  
Learning Rate ◽  
Time Varying ◽  
Optimal Value ◽  
Constraint Set ◽  
Time Varying Networks ◽  
Subgradient Algorithms ◽  
Asymptotic Upper Bound

We consider a distributed constrained optimization problem over a time-varying network, where each agent only knows its own cost functions and its constraint set. However, the local constraint set may not be known in advance or consists of huge number of components in some applications. To deal with such cases, we propose a distributed stochastic subgradient algorithm over time-varying networks, where the estimate of each agent projects onto its constraint set by using random projection technique and the implement of information exchange between agents by employing asynchronous broadcast communication protocol. We show that our proposed algorithm is convergent with probability 1 by choosing suitable learning rate. For constant learning rate, we obtain an error bound, which is defined as the expected distance between the estimates of agent and the optimal solution. We also establish an asymptotic upper bound between the global objective function value at the average of the estimates and the optimal value.

scholarly journals Jacobi Algorithm for the Best Low Multilinear Rank Approximation of Symmetric Tensors

2013 ◽  
Vol 34 (2) ◽  
pp. 651-672 ◽  
Author(s):  
Mariya Ishteva ◽  
P.-A. Absil ◽  
Paul Van Dooren
Keyword(s):  
Symmetric Tensors ◽  
Jacobi Algorithm ◽  
Multilinear Rank ◽  
Rank Approximation

scholarly journals Convergence rates of learning algorithms by random projection

2014 ◽  
Vol 37 (1) ◽  
pp. 36-51
Author(s):  
Di-Rong Chen ◽  
Han Li
Keyword(s):  
Convergence Rates ◽  
Learning Algorithms ◽  
Random Projection

scholarly journals Structure-Preserving Low Multilinear Rank Approximation of Antisymmetric Tensors

2017 ◽  
Vol 38 (3) ◽  
pp. 967-983 ◽  
Author(s):  
Erna Begović Kovač ◽  
Daniel Kressner
Keyword(s):  
Structure Preserving ◽  
Multilinear Rank ◽  
Rank Approximation
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