Kernel Sparsity Preserving Projections and Its Application to Biometrics

YIN Jun; YANG Wan-kou

doi:10.3969/j.issn.0372-2112.2013.04.003

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Kernel Sparsity Preserving Projections and Its Application to Biometrics

更新时间：2025-07-16

- Kernel Sparsity Preserving Projections and Its Application to Biometrics
- Acta Electronica Sinica Vol. 41, Issue 4, Pages: 639-645(2013)
- 作者机构：
  
  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
  3. 上海海事大学信息工程学院上海,201306
  4. 东南大学自动化学院江苏南京,210096
- 作者简介：
- 基金信息：
  
  Youth Fund of National Natural Science Foundation of China (No.61005008)
- DOI：10.3969/j.issn.0372-2112.2013.04.003
  CLC： TP391.4
- Published：2013
- 稿件说明：
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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：

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.

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.

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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