一种基于协作表示的判别局部保持投影方法

doi:10.3969/j.issn.1006-2475.2023.06.008

计算机与现代化 ›› 2023, Vol. 0 ›› Issue (06): 43-47.doi: 10.3969/j.issn.1006-2475.2023.06.008

一种基于协作表示的判别局部保持投影方法

李实秋

南京国电南自轨道交通工程有限公司研发中心,江苏南京 210000

收稿日期:2022-05-06 修回日期:2022-08-10 出版日期:2023-06-28 发布日期:2023-06-28
作者简介:李实秋（1986—）,男,陕西汉中人,工程师,硕士,研究方向：电力通信系统及图像处理,E-mail： lisq8385@163.com。
基金资助:
国家自然科学基金资助项目（61801363）

A Collaborative Representation-based Method of Discriminant Locality Preserving Projections

LI Shi-qiu

Department of R & D Center, Nanjing Guodian Nanzi Rail Transit Engineering Co., Ltd, Nanjing 210000, China

Received:2022-05-06 Revised:2022-08-10 Online:2023-06-28 Published:2023-06-28

摘要/Abstract

摘要： 判别局部保持投影（DLPP）算法是一种判别特征提取的方法,用于流形学习的典型降维算法,它能够利用判别信息,提取出最佳的判别特征,但是DLPP算法往往会忽略样本间的协作重构关系,从而导致算法识别率较低的问题。本文提出一种基于协作表示的判别局部保持投影（CRDLPP）方法。该方法首先利用协作表示方法对所有训练样本的协作重构误差进行计算,然后将该重构误差值作为正则项引入DLPP的目标函数中,最后通过求解新的目标优化问题得到最优投影矩阵。为了验证CRDLPP算法在图像识别方面的有效性,选取Yale人脸库和COIL20图像库进行算法仿真实验,结果表明,本文的CRDLPP算法在图像识别方面相比其他经典降维算法有着较高的识别率。

关键词: 判别局部保持投影, 协作表示, 协作重构误差, 图像识别

Abstract: Discriminant locality preserving projections （DLPP） is an effective discriminative feature extraction approach based on manifold learning. It is one of the typical representative algorithms of flow pattern dimensionality reduction. It can use the discriminant information to extract the best discriminant features. However, it fails to exploit the collaborative reconstruction relationship between samples, and thus usually leads to a lower recognition rate. To cope with this problem, a collaborative representation based discriminant locality preserving projections （CRDLPP） is proposed. CRDLPP first calculates the collaborative reconstruction errors of all the samples by collaborative representation mechanism, and then incorporates them as a regularization term into the objective function of DLPP. The optimal projection matrix is finally obtained by solving the new objective optimization problem. In order to verify the good performance of CRDLPP method in image recognition, this paper selects public image databases such as Yale and coil20 for experiments. The results show that the CRDLPP algorithm in this paper has a higher recognition rate than other classical data dimensionality reduction algorithms in image recognition.

Key words: discriminant locality preserving projections, collaborative representation, collaborative reconstruction error, image recognition

中图分类号:

TP391.41

李实秋. 一种基于协作表示的判别局部保持投影方法[J]. 计算机与现代化, 2023, 0(06): 43-47.

LI Shi-qiu. A Collaborative Representation-based Method of Discriminant Locality Preserving Projections[J]. Computer and Modernization, 2023, 0(06): 43-47.

参考文献 32

[1]	RON K, JOHN G H.Wrappers for feature subset selection[J]. Artificial Intelligence, 1997,97(1-2):273-324.
[2]	YUILLE A L, HALLINAN P W, COHEN D S.Feature extraction from faces using deformable templates[J]. International Journal of Computer Vision, 1992,8(2):99-111.
[3]	BELKIN M, NIYOGI P.Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 2003,15(6):1373-1396.
[4]	HE Z X,WU M T, ZHAO X Y, et al.Representative null space LDA for discriminative dimensionality reduction[J]. Pattern Recognition, 2021,111:107664.
[5]	WANG J Y, XIE F Y, NIE F P, er al. Unsupervised adaptive embedding for dimensionality reduction[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021.
[6]	WANG R, NIE F P, HONG R C, et al.Fast and orthogonal locality preserving projections for dimensionality reduction[J]. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 2017,26(10):5019-5030.
[7]	ZHANG Y P, XIANG M, YANG B.Linear dimensionality reduction based on Hybrid structure preserving projections[J]. Neurocomputing, 2016,173:518-529.
[8]	ZHAO H, WANG Z, NIE F.A new formulation of linear discriminant analysis for robust dimensionality reduction[J]. IEEE Transactions on Knowledge and Data Engineering, 2019,31(4):629-640.
[9]	TURK M, PENTLAND A.Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience, 1991,3(1):71-86.
[10]	BELHUMEUR P N, HESPANHA J P, KRIEGMAN D J.Eigenfaces vs. fisherfaces: Recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7) :711-720.
[11]	MARTINEZ A M, KAK A C.PCA versus LDA[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002,23(2):228-233.
[12]	MIAZONZAMA J F, HUA Q, WANG L.Face recognition using laplacianfaces and artificial neural network[J]. Applied Mechanics and Materials, 2015,3827(734):633-636.
[13]	HE X F, NIYOGI P.Locality preserving projections[J]. Advances in Neural Information Processing Systems, 2003,16(1):186-197.
[14]	HE X F, CAI D, YAN S C, et al.Neighborhood preserving embedding[C]// The 10th IEEE International Conference on Computer Vision (ICCV'05). 2005:1208-1213.
[15]	YANG J, ZHANG D, YANG J Y, et al.Globally maximizing, locally minimizing: Unsupervised discriminant projection with applications to face and palm biometrics[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007,29(4):650-664.
[16]	WANG H X, CHEN S B, HU Z L, et al.Locality-preserved maximum information projection[J]. IEEE Transactions on Neural Networks, 2008,19(4):571-585.
[17]	WANG Q, QIN Z, NIE F, et al.C2DNDA: A deep framework for nonlinear dimensionality reduction[J]. IEEE Transactions on Industrial Electronics, 2021,68(2):1684-1694.
[18]	WANG Y, YAO H X, ZHAO S C.Auto-encoder based dimensionality reduction[J]. Neurocomputing, 2016,184(4):232-242.
[19]	徐小莹,李辉. 基于深度自编码器的学术期刊综合评价研究[J]. 情报科学, 2019,37(12):71-77.
[20]	WU W, YU Z, HE J.A semi-supervised deep network embedding approach based on the neighborhood structure[J]. Big Data Mining and Analytics, 2019,2(3):205-216.
[21]	YU W, TENG X, LIU C.Face recognition using discriminant locality preserving projections[J]. Image and Vision Computing,2006,24(3):239-248.
[22]	HU H, FENG D, CHEN Q.A novel dimensionality reduction method: Similarity order preserving discriminant analysis[J]. Signal Processing, 2020,182. DOI: 10.1016/j.sigpro.2020.107933.
[23]	LI H F, JIANG T, ZHANG K S.Efficient and robust feature extraction by maximum margin criterion[J]. IEEE Transactions on Neural Networks, 2006,17(1):157-165.
[24]	ZHANG L, YANG M, FENG X C, et al. Collaborative representation based classification for face recognition[J]. arXiv preprint arXiv:1204.2358, 2012.
[25]	ZHANG L, YANG M, FENG X C.Sparse representation or collaborative representation: Which helps face recognition?[C]// 2011 IEEE International Conference on Computer Vision (ICCV 2011). 2011:471-478.
[26]	YANG W, WANG Z, SUN C.A collaborative representation based projections method for feature extraction[J]. Pattern Recognition, 2015,48(1):20-27.
[27]	CHEN S, DING C H Q, LUO B. Linear regression based projections for dimensionality reduction[J]. Information Sciences, 2018,467:74-86.
[28]	WANG G, SHI N.Collaborative representation-based discriminant neighborhood projections for face recognition[J]. Neural Computing and Applications, 2020,32(10):5815-5832.
[29]	PENG X, YI Z, TANG H.Robust subspace clustering via thresholding ridge regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2015,29(1):3827-3833.
[30]	BELHUMEUR P, KRIEGMAN D.The Yale face database[DB]. 1997.
[31]	NENE S A, NAYAR S K, MURASE H.Columbia object image library (COIL-20)[DB]. 1996.
[32]	PATRICK E A, FISCHER F P.A generalized k-nearest neighbor rule[J]. Information & Control, 1970,16(2):128-152.

一种基于协作表示的判别局部保持投影方法

A Collaborative Representation-based Method of Discriminant Locality Preserving Projections

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