[1]钱阳,李雷. 基于新型鲁棒字典学习的视频帧稀疏表示[J].计算机技术与发展,2017,27(02):37-41.
　QIAN Yang,LI Lei. Sparse Representation of Video Frame Based on Novel Robust Dictionary Learning[J].,2017,27(02):37-41.
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 基于新型鲁棒字典学习的视频帧稀疏表示()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年02期
页码:
37-41
栏目:
智能、算法、系统工程
出版日期:
2017-02-10

文章信息/Info

Title:
 Sparse Representation of Video Frame Based on Novel Robust Dictionary Learning
文章编号:
1673-629X（2017)02-0037-05
作者:
 钱阳; 李雷
 南京邮电大学视觉认知计算与应用研究中心
Author(s):
 QIAN Yang; LI Lei
关键词:
 ; 字典学习; 稀疏表示; 异常数据; 鲁棒性
Keywords:
 ; dictionary learning; sparse representation; outlier data; robustness
分类号:
TP301
文献标志码:
A
摘要:
 字典学习方法是一种非常有效的信号稀疏表示方法,在稀疏信号处理领域应用极其广泛.然而,实际应用中,训练样本和测试样本可能会受到损坏并且含有噪声和异常值,这将严重影响字典学习方法的性能.为此,不同于传统的字典学习方法从干净数据中学习字典,提出一种新型鲁棒字典学习算法,旨在处理训练样本中的异常值.该算法通过采用交替近端线性化方法求解非凸的最小l0范数,在学习鲁棒字典的同时隔离训练样本中的异常值.大量仿真对比实验表明,所提算法具有更好的鲁棒性,并能提供很好的性能改进.
Abstract:
 Dictionary learning is a very effective signal sparse representation method which has been widely used in the field of sparse signal processing.However,in practice,both training and testing samples may be corrupted and contain noises and a few outlier data,which may heavily affect the learning performance of it.Hence,in contrast to the conventional dictionary learning methods that learn the dictionary from clean data,a novel robust dictionary learning algorithm is proposed to handle the outliers in training data.In the proposed algorithm,the alternating proximal linearized method is used for solving the non-convex l0 norm based dictionary learning problem.Thus,the robust dictionary can be learned and outliers can be isolated in the training samples simultaneously.The simulation experimental results demonstrate that the method has the promising robusness and can provide significant performance improvement.

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　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(02):34.
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　YU Kun,JING Xiao-yuan,WU Fei,et al. K-SVD Dictionary Learning Algorithm Based on Competitive Agglomeration[J].,2015,25(02):44.
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更新日期/Last Update: 2017-05-11