基于线性鉴别的无参数局部保持投影算法

doi:10.13232/j.cnki.jnju.2019.02.006

南京大学学报(自然科学版) ›› 2019, Vol. 55 ›› Issue (2): 211–220.doi: 10.13232/j.cnki.jnju.2019.02.006

基于线性鉴别的无参数局部保持投影算法

范　君^1,2，业巧林¹*，业　宁¹

1.南京林业大学信息科学技术学院，南京，210037；2.江苏工程职业技术学院建筑工程学院，南通，226007

接受日期:2018-12-07 出版日期:2019-04-01 发布日期:2019-03-31
通讯作者: 业巧林 E-mail:yqlcom@njfu.edu.cn
基金资助:
江苏省高等职业院校国内高级访问学者计划(2016GRFX013)，江苏省青蓝工程(2016－15)，校科研计划项目(GYKY/2017/5，GYKY/2017/12)，江苏省高校哲学社会科学研究项目(2018SJA1247)

Parameter－free locality preserving projection based on linear discriminant

Fan Jun^1,2，Ye Qiaolin¹*，Ye Ning¹

1.College of Information Science and Technology，Nanjing Forestry University，Nanjing，210037，China； 2．School of Civil Engineering，Jiangsu College of Engineering and Technology，Nantong，226007，China

Accepted:2018-12-07 Online:2019-04-01 Published:2019-03-31
Contact: Ye Qiaolin E-mail:yqlcom@njfu.edu.cn

摘要/Abstract

摘要： 针对局部保持投影算法的无监督性质和参数选择复杂性问题，结合线性鉴别分析算法，提出一种改进的有监督无参数局部保持投影算法(Linear Discriminant Supervised Parameter－free Locality Preserving Projection algorithm，LD－SPLPP). LD－SPLPP算法采用监督模式并使用广义Dice系数的方法构建近邻矩阵，有效避免LPP(Locality Preserving Projection)算法参数选择调整的问题. 新算法在UCI的八个低维度数据集和两个高维度人脸数据库上进行了实验，通过对数据的特征提取，采用最近邻分类法统计识别率，并分析了实验分类后的数据值与算法性能的关系. 上述实验过程中，将新算法与PCA，LDA，ULDA，OLDA，LPP，SPLPP，PSKLPP，PSLMM和EP－SLPP算法进行了对比，实验结果证明了LD－SPLPP在数据降维和特征提取方面的有效性．

关键词: 特征提取, 局部保持投影, 线性鉴别, 无参数近邻矩阵, 广义Dice系数

Abstract: In this paper，considering the character of unsupervised and complexity of parameter selection of the locality preserving projection algorithm，an improved Linear Discriminant Supervised Parameter－free Locality Preserving Projection(LD－SPLPP)algorithm was proposed. In order to avoid the problems of parameters selection and adjustment of Locality Preserving Projection(LPP)algorithm，LD－SPLPP constructs an affinity matrix under the supervised mode and uses generalized Dice coefficient. LD－SPLPP algorithm performed experiments based on eight kinds of low－dimension datasets of UCI and two kinds of high－dimension human face databases. LD－SPLPP algorithm carried out the feature extraction on the data，used nearest neighbor classifier to get correct recognition rate and analyzed the relationship between the value of the classified data and the performance of the algorithm. During the experiments，LD－SPLPP is compared with PCA，LDA，ULDA，OLDA，LPP，SPLPP，PSKLPP，PSLMM and EP－SLPP，and the experimental results demonstrate that the proposed method is effective on the feature extraction.

Key words: feature extraction, Locality Preserving Projection(LPP), linear discriminant, parameter－free affinity matrix, generalized Dice coefficient

中图分类号:

TP391.14

范　君, 业巧林, 业　宁. 基于线性鉴别的无参数局部保持投影算法[J]. 南京大学学报(自然科学版), 2019, 55(2): 211–220.

Fan Jun, Ye Qiaolin, Ye Ning. Parameter－free locality preserving projection based on linear discriminant[J]. Journal of Nanjing University(Natural Sciences), 2019, 55(2): 211–220.

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基于线性鉴别的无参数局部保持投影算法

Parameter－free locality preserving projection based on linear discriminant

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