Earticle

초록

영어

Signal sparse matrix structure, the degree of relationship between signal sparse
representation, which affect application of compression perception to the effect of
recovery reconstruction for signal. In order to solve this problem, a variety of dictionary
learning algorithm such as KSVD, OLM (Online dictionary learning method) should be
put forward. These algorithms used overlapping image blocks to build a dictionary,
produced a large number of sparse coefficients, resulting in a fitting and calculation too
slowly, and cannot ensure convergence. Based on this, it designed a fast dictionary
learning algorithm based on proximal gradient. Algorithm based on the analysis of
proximal gradient multiple, on the basis of convex optimization problem, applied to the
dictionary learning involved in solving optimization, reduce the complexity of each
iteration, reduces the iterative overhead, at the same time to ensure the convergence.
Experiments on synthetic data show that the proposed algorithm dictionary learning
speed, the time is short, and obtain a better dictionary.

목차

Abstract
 1. Introduction
 2. The Dictionary Learning
 3. Fast Dictionary to be Learn based on the Proximal Gradient
  3.1. Accelerate the Proximal Gradient Method Based on Block
  3.2. The Dictionary to Learn
  3.3. Convergence Analysis
 4. Numerical Experiments
 5. Conclusion
 References

키워드

저자

• WenchunYu [ College of Computer science，NeiJiang Normal University, NeiJiang Sichuan, china ]
• Fei Fang [ Engineering and Technology College，NeiJiang Normal University, NeiJiang, Sichuan, china ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605