Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses-Reference-Cited by-同舟云学术

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Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses

Published:2022 Issue: Volume:70 Page:1803-1818
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Pu Wenqiang¹^ORCID,Ibrahim Shahana²^ORCID,Fu Xiao²^ORCID,Hong Mingyi³^ORCID

Affiliation:

1. Shenzhen Research Institute of Big Data, Shenzhen, China

2. Electrical Engineering and Computer Science Department, Oregon State University, Corvallis, OR, USA

3. Electrical and Computer Engineering Department, University of Minnesota, Minneapolis, MN, USA

Funder

NSFC, China

NSF ECCS

ARO

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/78/9675017/9745762-aam.pdf

Reference60 articles.

1. Stochastic model-based minimization under high-order growth;davis,2018

2. “Relative Continuity” for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent

3. The enron email dataset database schema and brief statistical report;shetty,2004

4. Fiber-Sampled Stochastic Mirror Descent for Tensor Decomposition with β-Divergence

5. Stochastic Block Mirror Descent Methods for Nonsmooth and Stochastic Optimization

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