Robust Flexible Preserving Embedding

被引:18
|
作者
Lu, Yuwu [1 ,2 ,3 ,4 ]
Wong, Wai Keung [4 ,5 ]
Lai, Zhihui [1 ,2 ,3 ,4 ]
Li, Xuelong [6 ,7 ]
机构
[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518055, Peoples R China
[2] Shenzhen Univ, Lab Intelligent Informat Proc, Shenzhen 518060, Peoples R China
[3] Shenzhen Univ, Guangdong Lab Artificial Intelligence & Digital E, Shenzhen 518060, Peoples R China
[4] Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Peoples R China
[5] Hong Kong Polytech Univ, Shenzhen Res Inst, Shenzhen 518054, Peoples R China
[6] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China
[7] Northwestern Polytech Univ, Ctr Opt Imagery Anal & Learning, Xian 710072, Peoples R China
基金
中国国家自然科学基金;
关键词
Manifolds; Principal component analysis; Feature extraction; Robustness; Optimization; Noise measurement; Sparse matrices; flexible; nuclear norm; robust; DISCRIMINANT-ANALYSIS; IMAGE; REDUCTION; GRAPH;
D O I
10.1109/TCYB.2019.2953922
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Neighborhood preserving embedding (NPE) has been proposed to encode overall geometry manifold embedding information. However, the class-special structure of the data is destroyed by noise or outliers existing in the data. To address this problem, in this article, we propose a novel embedding approach called robust flexible preserving embedding (RFPE). First, RFPE recovers the noisy data by low-rank learning and obtains clean data. Then, the clean data are used to learn the projection matrix. In this way, the projective learning is totally unaffected by noise or outliers. By encoding a flexible regularization term, RFPE can keep the property of the data points with a nonlinear manifold and be more flexible. RFPE searches the optimal projective subspace for feature extraction. In addition, we also extend the proposed RFPE to a kernel case and propose kernel RFPE (KRFPE). Extensive experiments on six public image databases show the superiority of the proposed methods over other state-of-the-art methods.
引用
收藏
页码:4495 / 4507
页数:13
相关论文
共 50 条
  • [1] Flexible constrained sparsity preserving embedding
    Weng, L.
    Dornaika, F.
    Jin, Z.
    [J]. PATTERN RECOGNITION, 2016, 60 : 813 - 823
  • [2] Flexible Orthogonal Neighborhood Preserving Embedding
    Pang, Tianji
    Nie, Feiping
    Han, Junwei
    [J]. PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2017, : 2592 - 2598
  • [3] Robust Attribute and Structure Preserving Graph Embedding
    Hettige, Bhagya
    Wang, Weiqing
    Li, Yuan-Fang
    Buntine, Wray
    [J]. ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2020, PT II, 2020, 12085 : 593 - 606
  • [4] Robust Attributed Network Embedding Preserving Community Information
    Liu, Yunfei
    Liu, Zhen
    Feng, Xiaodong
    Li, Zhongyi
    [J]. 2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022), 2022, : 1874 - 1886
  • [5] A Robust Fault Detection Method Based on Neighborhood Preserving Embedding
    Sha, Xin
    Luo, Chaomin
    Diao, Naizhe
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
  • [6] Identity-Preserving Adversarial Training for Robust Network Embedding
    Cen, Ke-Ting
    Shen, Hua-Wei
    Cao, Qi
    Xu, Bing-Bing
    Cheng, Xue-Qi
    [J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2024, 39 (01) : 177 - 191
  • [7] Robust multi-view locality preserving regression embedding
    Jing, Ling
    Li, Yi
    Zhang, Hongjie
    [J]. PeerJ Computer Science, 2024, 10 : 1 - 28
  • [8] Flexible and Robust Privacy-Preserving Implicit Authentication
    Domingo-Ferrer, Josep
    Wu, Qianhong
    Blanco-Justicia, Alberto
    [J]. ICT SYSTEMS SECURITY AND PRIVACY PROTECTION, 2015, 455 : 18 - 34
  • [9] Robust energy preserving embedding for multi-view subspace clustering
    Li, Haoran
    Ren, Zhenwen
    Mukherjee, Mithun
    Huang, Yuqing
    Sun, Quansen
    Li, Xingfeng
    Chen, Liwan
    [J]. KNOWLEDGE-BASED SYSTEMS, 2020, 210
  • [10] Robust dynamic process monitoring based on sparse representation preserving embedding
    Xiao, Zhibo
    Wang, Huangang
    Zhou, Junwu
    [J]. JOURNAL OF PROCESS CONTROL, 2016, 40 : 119 - 133