Collaborative Representation Based Discriminant Local Preserving Projection

被引:2
作者
Su, Tingting [1 ]
Feng, Dazheng [1 ]
Hu, Haoshuang [1 ]
机构
[1] Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Dimensionality reduction; Collaborative representation; Graph embedding; Data classification; ROBUST FEATURE-EXTRACTION; FACE-RECOGNITION; DIMENSIONALITY REDUCTION; SPARSE REPRESENTATION; ILLUMINATION; INFORMATION; EFFICIENT;
D O I
10.1007/s11063-022-10798-6
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Linear dimensionality reduction techniques have been applied widely in data classification and recognition to extract low-dimensional features. The methods exploit a simple linear function to transform high dimensional data into a low dimensional subspace while preserving the statistical or geometrical characteristics of high dimensional datasets. The neighborhood relationship is one of the most important geometrical characteristics. The original dimensionality reduction algorithms usually set neighborhood parameters manually when defining neighborhood relationships. However, the methods based on collaborative representation select the neighbors automatically. A supervised dimensionality reduction method proposed in this paper is named Collaborative Representation based Discriminant Local Preserving Projection (CR-DLPP). First, it uses collaborative representation to select potential neighbors automatically for samples reconstruction. Then, a similarity matrix is built by calculating the Gaussian distance between the reconstructed samples. Finally, the Maximum Margin Criterion (MMC) is adopted to design an objective function, and the optimal projection matrix is obtained via eigenvalue decomposition. The results of extensive experiments on several benchmark datasets show that CR-DLPP can achieve better performance than several other typical linear dimensionality reduction methods.
引用
收藏
页码:3999 / 4026
页数:28
相关论文
共 55 条
[1]   Face recognition using supervised probabilistic principal component analysis mixture model in dimensionality reduction without loss framework [J].
Ahmadkhani, Somaye ;
Adibi, Peyman .
IET COMPUTER VISION, 2016, 10 (03) :193-201
[2]  
[Anonymous], 2016, IEEE TPAMI, DOI DOI 10.1109/TPAMI.2015.2491929
[3]   Overview and comparative study of dimensionality reduction techniques for high dimensional data [J].
Ayesha, Shaeela ;
Hanif, Muhammad Kashif ;
Talib, Ramzan .
INFORMATION FUSION, 2020, 59 :44-58
[4]   Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection [J].
Belhumeur, PN ;
Hespanha, JP ;
Kriegman, DJ .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1997, 19 (07) :711-720
[5]   Graph Embedded Nonparametric Mutual Information For Supervised Dimensionality Reduction [J].
Bouzas, Dimitrios ;
Arvanitopoulos, Nikolaos ;
Tefas, Anastasios .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (05) :951-963
[6]  
Cai S, 2016, P IEEE C COMPUTER VI
[7]   A new LDA-based face recognition system which can solve the small sample size problem [J].
Chen, LF ;
Liao, HYM ;
Ko, MT ;
Lin, JC ;
Yu, GJ .
PATTERN RECOGNITION, 2000, 33 (10) :1713-1726
[8]   Face Recognition via Collaborative Representation: Its Discriminant Nature and Superposed Representation [J].
Deng, Weihong ;
Hu, Jiani ;
Guo, Jun .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (10) :2513-2521
[9]   From few to many: Illumination cone models for face recognition under variable lighting and pose [J].
Georghiades, AS ;
Belhumeur, PN ;
Kriegman, DJ .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2001, 23 (06) :643-660
[10]   Collaborative representation-based locality preserving projections for image classification [J].
Gou, Jianping ;
Yang, Yuanyuan ;
Liu, Yong ;
Yuan, Yunhao ;
Du, Lan ;
Yang, Hebiao .
JOURNAL OF ENGINEERING-JOE, 2020, 2020 (13) :310-315