Low-rank Tensor Completion via Tucker Decompositions
- Release time:2024-08-09
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Affiliation of Author(s):
理学院
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Journal:
Journal of Computational Information Systems
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Key Words:
中文关键字：张量补全，英文关键字：Tensor Completion;
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Abstract:
Tensor nuclear norm minimization (TNNM) is a commonly-used model for solving low-rank tensor
completion (LRTC) problems. Generally, algorithms to TNNM have very heavy computation burden due
to the involvement of multiple singular value decompositions (SVDs) at each loop. To address efficiently
LRTC, this paper proposes a Tucker decompositions technique which adopts the thin QR decompositions
instead of SVDs. First, we establish a minimization model for LRTC based on Tucker decompositions and
analyze its first-order optimality conditions. Then, we develop an iterative algorithm to the proposed
optimization problem and prove its convergence. Finally, experimental results demonstrate that our
method is competitive to other existing methods in computation complexity, completion accuracy and
compression performance.
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Note:
EI
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Co-author:
雍龙泉
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First Author:
zhengxiuyun,yangwei,shijiarong
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Indexed by:
Journal paper
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Volume:
卷：11
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Issue:
期：10
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Page Number:
页：3759-3768
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Translation or Not:
no
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Date of Publication:
2015-05-01
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