Two-dimensional Fisher discriminant analysis: forget about small sample size problem [face recognition applications]

By Hui Kong, Eam Khwang Teoh, Jian-Gang Wang, R. Venkateswarlu. 2005

38 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Shuicheng Yan
2. Dacheng Tao
3. Luc Van Gool
4. Lei Zhang
5. Yi Yang
6. Ming-Hsuan Yang
7. Xiaogang Wang
8. Trevor Darrell

In Media Technology

1. Lei Zhang
2. Xiaoou Tang
3. Shuicheng Yan
4. Qiang Yang
5. HongJiang Zhang
6. Dong Xu
7. Wangmeng Zuo
8. Kai Zhang

In Statistics and Probability

1. Shuicheng Yan
2. Qiang Yang
3. Lei Zhang
4. Dong Xu
5. Xiaoou Tang

Citations in the corpus

• Tensor representation in high-frequency financial data for price change prediction (2017, neutral): “With advances in mathematical tools and algorithms dealing with tensor input, many multilinear discriminant techniques as well as tensor regression have been proposed for image and video classification problems such as [18], [19], [20], [21], [22], [23], [24], [25], [26], [27].”
• Multilinear Discriminant Analysis for Face Recognition (2007, methodology): “[7] proposed an algorithm called 2-D FDA, with the similar idea as 2-D LDA, for dimensionality reduction and showed its advantages in small sample size problem.”