Robust non-negative supervised low-rank discriminant embedding algorithm

Yu YAO; Minghua WAN

doi:10.11959/j.issn.2096-6652.202135

您当前的位置：

首页 >

文章列表页 >

Robust non-negative supervised low-rank discriminant embedding algorithm

Special Issue: Intelligent Object Detection and Recognition | 更新时间：2024-06-05

- Robust non-negative supervised low-rank discriminant embedding algorithm
- Chinese Journal of Intelligent Science and Technology Vol. 3, Issue 3, Pages: 342-350(2021)
- 作者机构：
  
  1. 南京审计大学信息工程学院，江苏南京 211815
  2. 江苏省社会安全图像与视频理解重点实验室，江苏南京 210094
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202135
  CLC： TP391.4
- Published Online：2021-09，
  
  Published：15 September 2021
- 稿件说明：
移动端阅览
Yu YAO, Minghua WAN. Robust non-negative supervised low-rank discriminant embedding algorithm[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 342-350.
DOI：

Yu YAO, Minghua WAN. Robust non-negative supervised low-rank discriminant embedding algorithm[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 342-350. DOI： 10.11959/j.issn.2096-6652.202135.

摘要

非负矩阵分解（NMF）已经得到了广泛应用。但NMF更注重数据的局部信息，忽略了数据的全局信息，而在有噪声图像的分类问题上，数据的全局信息往往比局部信息更具鲁棒性。为了提高算法的鲁棒性，结合数据的局部与全局信息，并且考虑低秩表示的特性，提出了一种新的非负监督低秩鉴别嵌入算法，此算法假设数据存在噪声，将数据分解为干净数据与噪声数据，并通过L

范数对噪声矩阵进行稀疏约束，增强对噪声的鲁棒性。此外，该算法使用低秩表示学习到一个低秩矩阵，然后通过非负分解再一次增强算法的鲁棒性。最后结合图嵌入理论，同时保留了数据的局部性和全局性。将该算法应用于各种加噪数据库中发现，相对于对比方法，该算法识别率提升了5%～15%。

Abstract

Non-negative matrix factorization (NMF) has been widely used.However

NMF pays more attention to the local information of the data

it ignores the global representation of the data.In terms of image classification

the global information of the data is often more robust to noise than the local information.In order to improve the robustness of the algorithm

combined with the data of local and global representation

and considered the characteristics of low-rank representation

a non-negative supervised low-rank discriminant embedded algorithm was proposed.This algorithm assumed the existence of noise in the data

decomposed the data into clean data and noise data

and made sparse constraints on the noise matrix through the L

norm

so as to enhance the robustness to noise.In addition

the algorithm used low-rank representation to learn a low-rank matrix

then through non-negative decomposition

the robustness of the algorithm was enhanced again.Finally

combined with a study of graph embedding method

the local and global data were retained at the same time.The algorithm is applied to various noisy databases

and the recognition rate of this algorithm is improved by about 5%～15% compared with the comparison algorithm.

关键词

Keywords

references

任克强 , 张静然 . 基于局部Fisher准则判别投影的人脸识别算法 [J ] . 传感器与微系统 , 2019 , 38 ( 7 ): 113 - 116 , 120 .

REN K Q , ZHANG J R . Face recognition algorithm based on local Fisher criterion discriminant projections [J ] . Transducer and Microsystem Technologies , 2019 , 38 ( 7 ): 113 - 116 , 120 .

YANG J , ZHANG D , YANG J Y . Constructing PCA baseline algorithms to reevaluate ICA-based face-recognition performance [J ] . IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics) , 2007 , 37 ( 4 ): 1015 - 1021 .

刘文华 , 李浥东 , 王涛 , 等 . 基于高维特征表示的交通场景识别 [J ] . 智能科学与技术学报 , 2019 , 1 ( 4 ): 392 - 399 .

LIU W H , LI Y D , WANG T , et al . Transportation scene recognition based on high level feature representation [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 4 ): 392 - 399 .

ZUO W M , ZHANG D , YANG J , et al . BDPCA plus LDA:a novel fast feature extraction technique for face recognition [J ] . IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics) , 2006 , 36 ( 4 ): 946 - 953 .

NIE F P , WANG Z , WANG R , et al . Adaptive local linear discriminant analysis [J ] . ACM Transactions on Knowledge Discovery from Data , 2020 , 14 ( 1 ): 1 - 19 .

HOYER P O , . Non-negative sparse coding [C ] // Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing . Piscataway:IEEE Press , 2002 : 557 - 565 .

CAI D , HE X F , HAN J W , et al . Graph regularized nonnegative matrix factorization for data representation [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2011 , 33 ( 8 ): 1548 - 1560 .

GUAN N Y , TAO D C , LUO Z G , et al . Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent [J ] . IEEE Transactions on Image Processing , 2011 , 20 ( 7 ): 2030 - 2048 .

杜海顺 , 张旭东 . 图嵌入正则化投影非负矩阵分解人脸图像特征提取 [J ] . 中国图象图形学报 , 2014 , 19 ( 8 ): 1176 - 1184 .

DU H S , ZHANG X D . Graph embedding regularized projective non-negative matrix factorization for face image feature extraction [J ] . Journal of Image and Graphics , 2014 , 19 ( 8 ): 1176 - 1184 .

ZHANG L J , CHEN Z G , ZHENG M , et al . Robust non-negative matrix factorization [J ] . Frontiers of Electrical and Electronic Engineering in China , 2011 , 6 ( 2 ): 192 - 200 .

KONG D G , DING C , HUANG H . Robust nonnegative matrix factorization using L 2.1 -norm [C ] // Proceedings of the 20th ACM International Conference on Information and Knowledge Management . New York:ACM Press , 2011 .

WRIGHT J , MA Y , MAIRAL J , et al . Sparse representation for computer vision and pattern recognition [J ] . Proceedings of the IEEE , 2010 , 98 ( 6 ): 1031 - 1044 .

WRIGHT J , YANG A Y , GANESH A , et al . Robust face recognition via sparse representation [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2009 , 31 ( 2 ): 210 - 227 .

YANG M , ZHANG L . Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary [C ] // Proceedings of the European Conference on Computer Vision .[S.l.:s.n. ] , 2010 .

LIU G C , LIN Z C , YAN S C , et al . Robust recovery of subspace structures by low-rank representation [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2013 , 35 ( 1 ): 171 - 184 .

LIU G C , LIN Z C , YONG Y . Robust subspace segmentation by low-rank representation [C ] // Proceedings of the International Conference on Machine Learning .[S.l.:s.n. ] , 2003 : 1615 - 1618 .

LIU G C , LIN Z C , YONG Y . Robust subspace segmentation by low-rank representation [C ] // Proceedings of the ICML .[S.l.:s.n. ] , 2010 : 663 - 670 .

MAZUMDER R , HASTIE T , TIBSHIRANI R . Spectral regularization algorithms for learning large incomplete matrices [J ] . The Journal of Machine Learning Research , 2010 , 11 : 2287 - 2322 .

Views

551

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on 6G network scenario cognition based on knowledge graph

Related Author

ZHAO Zhuoqiao

CHENG Nan

CHEN Jie

CHEN Fangjiong

LI Changle

Related Institution

College of Communication Engineering, Xidian University

National Key Laboratory of Communication, University of Electronic Science and Technology of China

School of Electronics and Information Engineering, South China University of Technology

Postal code：100079
Tel：（010）53879206 Email：cjist@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
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.
Total Visits：35685 Visits Today：160

⁰