Semi-supervised Bi-directional Dimensionality Reduction for Face Recognition

机译：对面部识别的半监督双向维度减少

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摘要

A novel method for face recognition called semi-supervised bi-directional dimensionality reduction (SSBDR) is proposed. Based on semi-supervised learning, domain knowledge in the form of pair wise constraints besides abundant unlabeled examples are available, which specifies whether a pair of instances belong to the same class or not. Compared to the semi-supervised dimensionality reduction (SSDR), it can not only preserve the intrinsic structure of the unlabeled data as well as both the must-link (the same class) and cannot-link constraints (different classes) defined on the labeled examples in the projected low-dimensional space, but also constructs two image covariance matrices directly by the original image matrix in two directions which can reduce the dimension of the original image matrix in two directions. The validity of this method can be verified by the experiments on ORL face database.

机译：提出了一种称为半监督双向维度减少（SSBDR）的面部识别方法。基于半监督学习，可以使用除了丰富的未标记示例之外的成对规定形式的域知识，这指定了一对实例是否属于同一类。与半监督维度减少（SSDR）相比，它不仅可以保留未标记数据的内在结构以及标记为标记的必须定义的必须链接（相同类）和无法链接约束（不同类别）投影的低维空间中的示例，但是还通过原始图像矩阵在两个方向上直接构造两个图像协方差矩阵，其可以在两个方向上降低原始图像矩阵的尺寸。可以通过ORL面部数据库的实验来验证该方法的有效性。

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《IEEE International Conference on Information and Automation》|2010年||共4页
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Lihua Wang; Chunjian Ren; Hongbo Xu; Chanchan Qin;
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中图分类 TP2-53;
关键词
Face recognition; Semi-supervised learning; Pattern classification; Principal component analysis; Dimension reduction;

机译：面部识别;半监督学习;图案分类;主成分分析;尺寸减少;

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1. A Novel Angle-Based Learning Framework on Semi-supervised Dimensionality Reduction in High-Dimensional Data with Application to Action Recognition [J] . Zahra Ramezani, Ahmad Pourdarvish, Kiumars Teymourian Arabian Journal for Science and Engineering. Section A, Sciences . 2020,第12期

机译：基于角度的学习框架，用于动作识别的高维数据的半监督维度减少
2. A noise-robust semi-supervised dimensionality reduction method for face recognition [J] . Gan Haitao Optik: Zeitschrift fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic . 2018,第期

机译：一种用于人脸识别的噪声稳健半监督维度减少方法
3. Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition [J] . Zhao Zhang, Mingbo Zhao, Tommy W.S. Chow Neural Networks: The Official Journal of the International Neural Network Society . 2013,第Null期

机译：具有信息约束的边缘半监督子流形投影，用于降维和识别
4. Semi-supervised Bi-directional Dimensionality Reduction for Face Recognition [C] . Lihua Wang, Chunjian Ren, Hongbo Xu, Proceedings of the 2010 IEEE International Conference on Information and Automation . 2010

机译：人脸识别的半监督双向降维
5. Using discriminative dimensionality reduction to understand the neural basis of recognition memory encoding and retrieval. [D] . Noh, Eunho. 2014

机译：使用判别降维来理解识别记忆编码和检索的神经基础。
6. Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning [O] . Mehmet Gönen -1

机译：监督和半监督多标签学习的降维和分类耦合
7. Effective Semi-supervised Nonlinear Dimensionality Reduction for Wood Defects Recognition [O] . Zhao Zhang, Ning Ye 2014

机译：有效半监督非线性降维在木材缺陷识别中的应用

Semi-supervised Bi-directional Dimensionality Reduction for Face Recognition

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