[1]余琨,荆晓远,吴飞,等. 基于竞争聚集的K-SVD字典学习算法[J].计算机技术与发展,2015,25(11):44-48.
　YU Kun,JING Xiao-yuan,WU Fei,et al. K-SVD Dictionary Learning Algorithm Based on Competitive Agglomeration[J].,2015,25(11):44-48.
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 基于竞争聚集的K-SVD字典学习算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年11期
页码:
44-48
栏目:
智能、算法、系统工程
出版日期:
2015-11-10

文章信息/Info

Title:
 K-SVD Dictionary Learning Algorithm Based on Competitive Agglomeration
文章编号:
1673-629X（2015)11-0044-05
作者:
 余琨; 荆晓远; 吴飞; 姚永芳
 南京邮电大学 自动化学院
Author(s):
 YU Kun; JING Xiao-yuan; WU Fei; YAO Yong-fang
关键词:
 ; 稀疏表示; 字典学习; 聚类; 竞争聚集; K-SVD算法
Keywords:
 ; sparse representation; dictionary learning; clustering; competitive agglomeration; K-SVD algorithm
分类号:
TP301.6
文献标志码:
A
摘要:
 在基于稀疏表示分类的模式识别中,字典学习可以为稀疏表示获得更为精简的数据表示.然而,字典大小是衡量识别精度和速度的重要因素,优化字典设计能同时满足这两方面的需求.文中提出了一种新的技术叫作基于竞争聚集的K奇异值字典学习方法(CA-KSVD).该方法优化了字典的大小,并同时保证了识别的准确率.CA-KSVD将竞争聚集算法中优化簇数的原理引入K-SVD,从而提高了K-SVD的字典学习能力.优化过程从输入大量字典原子开始,逐步减少那些未充分利用或相似的原子,最后得到高性能的字典,它不再包含那些冗余的原子.Extend YaleB和AR人脸数据库上的实验结果表明了文中算法的有效性.
Abstract:
 In pattern recognition based on sparse representation classification,concise representation of date can be obtained for sparse rep-resentation via dictionary learning. However,the size of dictionary is an important tradeoff between recognition speed and accuracy,the design of optimized dictionary can satisfy requirements of two aspects simultaneously. A novel technique called the K -SVD dictionary learning algorithm based on competitive agglomeration (CA-KSVD) is proposed,which finds a dictionary with optimized size without compromising its recognition accuracy. CA-KSVD improves the K-SVD dictionary learning algorithm by introducing a mechanism to K-SVD,the mechanism in competitive agglomeration can optimize dictionary size. Optimization procedure starts with a large number of dictionary atoms and gradually reduces the under-utilized or similar atoms to produce a high-performance dictionary that has no redun-dant atoms. Experimental results with Extend YaleB and AR databases demonstrate the effectiveness of the method in this paper.

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更新日期/Last Update: 2015-12-25