| 核稀疏保持投影及生物特征识别应用 |
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| 引用本文： | 殷俊,杨万扣. 核稀疏保持投影及生物特征识别应用[J]. 电子学报, 2013, 41(4): 639-645. DOI: 10.3969/j.issn.0372-2112.2013.04.003 |
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| 作者姓名： | 殷俊 杨万扣 |
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| 作者单位： | 1. 上海海事大学信息工程学院,上海 201306;2. 东南大学自动化学院,江苏南京 210096 |
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| 基金项目： | 国家自然科学基金青年基金 |
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| 摘 要： | 稀疏表示系数包含较强的鉴别信息,稀疏保持投影(Sparsity Preserving Projections,SPP)利用稀疏表示系数进行特征提取.本文通过核方法获取高维特征空间的核稀疏表示系数,并利用核稀疏表示系数构造邻接矩阵,提出核稀疏保持投影(Kernel Sparsity Preserving Projections,KSPP).核稀疏表示系数比稀疏表示系数包含更强的鉴别信息,因此KSPP可以比SPP提取更有效的鉴别特征.在多个数据库上的生物特征识别实验,KSPP都取得了不错的实验结果.
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| 关 键 词： | 稀疏表示 邻接矩阵 稀疏保持投影 核方法 |
| 收稿时间： | 2012-05-31 |
Kernel Sparsity Preserving Projections and Its Application to Biometrics |
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| YIN Jun , YANG Wan-kou. Kernel Sparsity Preserving Projections and Its Application to Biometrics[J]. Acta Electronica Sinica, 2013, 41(4): 639-645. DOI: 10.3969/j.issn.0372-2112.2013.04.003 |
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| Authors: | YIN Jun YANG Wan-kou |
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| Affiliation: | 1. Shanghai Maritime University, College of Information Engineering, Shanghai 201306, China;2. Southeast University, School of Automation, Nanjing, Jiangsu 210096, China |
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| Abstract: | Sparse representation coefficient contains strong discriminant information and sparsity preserving projections extracts features by sparse representation coefficient.This paper obtains kernel sparse representation coefficient in the high dimensional space by kernel method and use kernel sparse representation coefficient to construct adjacency matrix,then propose kernel sparsity preserving projections.Kernel sparse representation coefficient contains stronger discriminant information than sparse representation coefficient;therefore,KSPP could extract more efficient features than SPP.KSPP achieves good results in biometrics experiments of several databases. |
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| Keywords: | sparse representation adjacency matrix sparsity preserving projections kernel method |
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