RLS 字典学习中遗忘因子的影响

doi:10.3969/j.issn.0255-8297.2014.01.008

应用科学学报 ›› 2014, Vol. 32 ›› Issue (1): 44-50.doi: 10.3969/j.issn.0255-8297.2014.01.008

RLS 字典学习中遗忘因子的影响

余付平1,2，冯有前1，雷腾1，李哲1

1. 空军工程大学，西安710051
2. 中国人民解放军94559部队，江苏徐州221000

收稿日期:2011-12-08 修回日期:2012-01-12 出版日期:2014-01-31 发布日期:2012-01-12
作者简介:余付平，博士生，研究方向：雷达信号处理、抗干扰研究，E-mail: junjingj@163.com；冯有前，教授，博导，研究方向：信号与信息处理，E-mail: fengyouqian123@163.com
基金资助:
国家自然科学基金(No. 61003148)资助

Effects of Forgetting Factor on RLS Dictionary Learning

YU Fu-ping1,2, FENG You-qian1, LEI Teng1, LI Zhe1

1. Air Force Engineering University, Xi’an 710051, China
2. 94559 Unit, Chinese People’s Liberation Army, Xuzhou 221000, Jiangsu Province, China

Received:2011-12-08 Revised:2012-01-12 Online:2014-01-31 Published:2012-01-12

摘要/Abstract

摘要： 字典学习是信号稀疏分解研究的热点问题. 在稀疏分解字典学习中，初始字典的选择影响字典学习的效果. 为减小初始字典对学习字典的影响，在递归最小二乘(recursive least squares, RLS) 字典学习方法中引入遗忘因子的概念. 比较了最优方向法、K 奇异值分解方法和RLS 等3 种方法的字典学习效果. 分析了RLS 字典学习中不同的遗传因子对字典学习效果的影响，以及遗忘因子为不同函数时的字典学习效果. 仿真结果表明：RLS 字典学习方法减小了初始字典对学习结果的影响，故学习效果较好；而在RLS 字典学习中不同遗忘因子的选择会影响字典学习效果.

关键词: 字典学习, 稀疏分解, 递归最小二乘, 遗忘因子

Abstract: Dictionary learning is a hot topic in signal sparse decomposition. The choice of the initial dictionary affects the result of dictionary learning. In order to reduce the effects, a forgetting factor is introduced into the recursive least squares (RLS) dictionary learning. The dictionary learning effects of three dictionary
learning methods, method of optimal directions (MOD), K singular value decomposition (KSVD), and RLS are compared. Influences of different fixed forgetting factors on the final learned dictionary are analyzed,and the results of dictionary learning with different forgetting factors studied. Simulation shows that the RLS dictionary learning reduces the influence of the initial dictionary, and gives better effects. Results of the dictionary learning are influenced by the choice of the forgetting factor.

Key words: dictionary learning, sparse decomposition, recursive least squares (RLS), forgetting factor

中图分类号:

TN911.7

余付平1,2，冯有前1，雷腾1，李哲1. RLS 字典学习中遗忘因子的影响[J]. 应用科学学报, 2014, 32(1): 44-50.

YU Fu-ping1,2, FENG You-qian1, LEI Teng1, LI Zhe1.
Effects of Forgetting Factor on RLS Dictionary Learning [J]. Journal of Applied Sciences, 2014, 32(1): 44-50.

参考文献

[1] 焦李成，杨淑媛，刘芳，侯彪. 压缩感知回顾与展望[J]. 电子学报, 2011, 39(7): 1651-1662. JIAO Licheng, YANG Shuyuan, LIU Fang, HOU Biao. Development and prospect of compressive sensing [J]. Acta Electronica Sinica, 2011, 39(7): 1651-1662. (in Chinese)

[2] SKRETTING K, ENGAN K. Image compression using learned dictionaries by RLS-DLA and compared with K-SVD[C]//Proc. IEEE International Conference Acoust., Speech, Signal Process (ICASSP), 2011, Pargue, Czech Republic, 2011:1517-1520.

[3] M. Elad. Sparse and redundant representations from theory to applications in signal and image processing [D]. New York: Springer, 2010.

[4] 邓承志，曹汉强. 多尺度脊波字典的构造及其在图像编码中的应用[J]. 图像图形学报，2009,14(7):1273-1278. (DENG Chengzhi, CAO Hanqiang. Construction of multi-scale ridgelet dictionary and its application for image coding[J]. Journal of Image and Graphics, 2009, 14(7):1273 -1278. (in Chinese)

[5] 孙玉宝，肖亮，韦志辉，邵文泽. 基于Gabor感知多成份字典的图像稀疏表示算法研究[J].自动化学报，2008,34(11):1379-1387. SUN Yubao, XIAO Liang, WEI Zhihui, SHAO Wenze. Sparse representations of images by a multi-component Gabor perception dictionary[J]. Acta Automatica Sinica, 2008, 34(11): 1379-1387. (in Chinese)

[6] SHEN Bin, HU Wei, ZHANG Yimin, ZHANG Yujin. Image inpainting via sparse representation [C]//Proc. IEEE International Conference Acoust., Speech, Signal Process. Taiwan, 2009: 697-700.

[7] 刘金江，王春光，孙即祥. 基于稀疏分解和神经网络的心电信号波形检测即识别[J]. 信号处理，2011,27(6):843-850. LIU Jinjiang, Wang Chunguang, SUN Jixiang. The detection and recognition of electrocardiogram’s waveform based on sparse decomposition and neural network[J]. Signal Processing,2011, 27(6): 843-850. (in Chinese)

[8] 杨清山，郭成安，金明录. 基于Gabor 多通道加权优化与稀疏表征的人脸识别方法[J]. 电子与信息学报，2011,33(7): 1618-1624. YANG Qingshan, GUO Chengan, JIN Minglu. Face recognition based on Gabor multi- channel weighted optimization and sparse representation[J]. Journal of Electronics & Information Technology, 2011, 33(7): 1618-1624. (in Chinese)

[9] 彭富强，于德介，刘坚. 一种基于多尺度线调频基的稀疏信号分解方法[J]. 振动工程学报，2010,23(3):333-338. PENG Fuqiang，YU Dejie，LIU Jian. Sparse signal decomposition method based on multi-scale chirplet [J]. Journal of Vibration Engineering, 2010, 23(3):333- 338. (in Chinese)

[10] Karl Skretting K, Kjersti Engan K. Recursive least squares dictionary learning algorithm [J]. IEEE Transactions on Signal Processing, 2010, 58(4): 2121-2130.

[11] Michal Aharon M, Michael Elad M, Alfred Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation [J]. IEEE Transactions on Signal Processing, 2006, 54 (11):4311- 4322.

[12] ENGAN K, AASE S O, HUSOY J H. Method of optimal directions for frame design [C]// Proc. IEEE International Conference Acoust., Speech, Signal Process. (ICASSP), 1999: 2443-2446.

[13] Maria G. JAFARI M G, Mark D. PLUMBLEY M D. Fast dictionary learning for sparse representations of speech signals [J]. IEEE Journal of Selected Topic in Signal Processing, 2011, 5(5):1025- 1031.

[14] Md. Mashud Hyder M M, Kaushik Mahata K. An improved smoothed approximation algorithm for sparse representation [J]. IEEE Transactions on Signal Processing, 2010, 58(4): 2194-2205.

RLS 字典学习中遗忘因子的影响

Effects of Forgetting Factor on RLS Dictionary Learning

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	贾永红1,2，吕臻1，周明婷1. 资源三号卫星影像超分辨率重建[J]. 应用科学学报, 2015, 33(3): 309-316.
[2]	彭岳星, 吴镇扬, 王维新, 刘陈. 频选瑞利快衰落信道下的迭代多用户检测算法[J]. 应用科学学报, 2005, 23(3): 245-250.
[3]	张承慧, 王划一, 张天德. 潮汐模拟系统的建模与自适应控制[J]. 应用科学学报, 1997, 15(4): 474-481.