Research on behavior identification based on SPLDA dimensional reduction algorithm and XGBoost classifier

YE Dan; LI Zhi; WANG Yong-jun

Chinese

您当前的位置：

首页 >

文章列表页 >

Research on behavior identification based on SPLDA dimensional reduction algorithm and XGBoost classifier

更新时间：2026-09-04

- Research on behavior identification based on SPLDA dimensional reduction algorithm and XGBoost classifier
- Vol. 36, Issue 6, Pages: 35-39(2019)
- 作者机构：
  
  1. 桂林电子科技大学电子工程与自动化学院,广西,桂林,541004
  2. 桂林航天工业学院无人机遥测重点实验室,广西,桂林,541004
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
移动端阅览
叶丹, 李智, 王勇军. 基于SPLDA降维和XGBoost分类器的行为识别方法研究[J]. 微电子学与计算机, 2019, 36(6): 35-39.

YE Dan, LI Zhi, WANG Yong-jun. Research on behavior identification based on SPLDA dimensional reduction algorithm and XGBoost classifier[J]. 2019, 36(6): 35-39.
叶丹, 李智, 王勇军. 基于SPLDA降维和XGBoost分类器的行为识别方法研究[J]. 微电子学与计算机, 2019, 36(6): 35-39. DOI：

YE Dan, LI Zhi, WANG Yong-jun. Research on behavior identification based on SPLDA dimensional reduction algorithm and XGBoost classifier[J]. 2019, 36(6): 35-39. DOI：

摘要

针对人体行为识别过程中分类算法识别精度低和数据样本集的"维数灾难"问题

提出了基于行为识别的SPLDA降维算法.首先

利用SPLDA算法在原有样本协方差矩阵不变的情况下获取最重要的主分量

通过贪婪搜索方法得到多个投影向量; 然后

通过更新类内散度矩阵获得最优转换矩阵; 最后

将降维后的样本数据集通过XGBoost分类器进行最终的行为识别.实验结果表明

XGBoost分类器与随机森林算法相比

平均识别精度提高了2.66%

识别时间降低了0.52 s; SPLDA-XGB算法可以实现有效降维且比PCA算法、LDA算法、LPP算法、L-PCA算法与XGBoost分类器结合的识别算法具有更高的人体行为识别准确率.

Abstract

Aiming at the problem of "dimension disaster" in human behavior recognition and the classification algorithm has low recognition accuracy and data sample set. First

the SPLDA algorithm is used to obtain the most important principal components with the original sample covariance matrix unchanged

and multiple projection vectors are obtained by greedy search method. Then

the optimal transformation matrix is obtained by updating the class inner divergence matrix. Finally

the dimensionally reduced sample data set is identified by the XGBoost classifier. Experimental results show that compared with the random forest algorithm

the average recognition accuracy of XGBoost classifier is improved by 2.66% and the recognition time is reduced by 0.52s. SPLDA-XGB algorithm can achieve effective dimensionality reduction and has higher accuracy rate of human behavior recognition than PCA algorithm

LDA algorithm

LPP algorithm

l-pca algorithm combined with XGBoost classifier.

关键词

Keywords

references

Chen T Q. XGBoost: A Scalable Tree Boosting System[C].In Proceeding KDD'16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco, 2016: 2-4.

VAN D M L, POSTMA E, VAN DENⅡERIK J.Dimension reduction: A comparative review[R].TiCCTR: Tiburg University, 2009.

Wang H X, Qin T, Zheng W M.L1-norm-based common spatial patterns.[J]. IEEE Transactions on Biomedical Engineering, 2012, 59(3): 653-662.

KWAPIAZ J R, WEISS G M, MOORE S A.Activ-ity recognition using cell phoneaccele ro-meters[J].ACM SigKDD Explorations Newsle-tter, 2011, 12(2):74-82. DOI: 74

YANG J, WRIGHT J, HUANG T S, et al.Image s-uperresolution via sparse representation[J].IEEE Transactions on Image ProcessingA Publication of the IEEE Signal Pocessing So-ciety, 2010, 19(11):28-128. DOI: 28

李龙龙, 何东健, 王美丽.L-PCA算法下的高维图像降维算法研究[J].西安科技大学学报, 2017, 37(6):906-911. DOI: 906

KHAN A M, TUFAIL A, KHATTAK A M.Ativity recognition on smartphones via sensor-fusion and KDA-based SVMs[J]. International Journal of Distributed Sensor Networks, 2014(1):1-14. DOI: 1

钟福金.鲁棒线性子空间学习算法与框架研究[D]: 成都: 西南交通大学, 2015.

聂军.基于K-L特征压缩的云计算冗余数据降维算法[J].微电子学与计算机, 2016, 33(2):125-129. DOI: 125

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

Design of ultra-low power real-time clock circuits

Aerial small object detection algorithm based on improved RT-DETR

Microservices anomaly detection based on the correlation of monitoring metrics

Lightweight countermeasures against Spectre attacks on the branch prediction unit

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰