Solving the small sample size problem of LDA

By Rui Huang, Qingshan Liu, Hanqing Lu, Songde Ma. Object recognition supported by user interaction for service robots, 2002

304 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/54f19dd631e7815aef4b51f85d25c621cfa6d2e1

How researchers see this paper

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

In Computational Mechanics

1. Xiaoyang Tan
2. Daoqiang Zhang
3. Songcan Chen
4. Jun Liu

In Computer Vision and Pattern Recognition

1. Songcan Chen
2. Daoqiang Zhang
3. Jun Liu
4. Xiaoyang Tan
5. Matti Pietikäinen
6. Dacheng Tao
7. Xilin Chen
8. Abdenour Hadid

In Signal Processing

1. Xiaoyang Tan
2. Daoqiang Zhang
3. Songcan Chen
4. Abdenour Hadid
5. Sébastien Marcel

Citations in the corpus

• Efficient and robust feature extraction by maximum margin criterion (2003, methodology): “The authors would like to thank the associate editor and the anonymous reviewers for the constructive comments.”
• Two-dimensional Fisher discriminant analysis: forget about small sample size problem [face recognition applications] (2005, unknown): “2D FDA 2D PCA N LDA [7] FLDA [3] D LDA [6]”
• Linear discriminant analysis: A detailed tutorial (2017, unknown): “This problem results from high-dimensional pattern classification tasks or a low number of training samples available for each class compared with the dimensionality of the sample space [30,38,82, 85].”
• Linear discriminant analysis for the small sample size problem: an overview (2014, unknown): “Based on these distinct combinations, we categorize the following LDA-SSS techniques into one of the four categories: null LDA (NLDA) [9], PCA ? NLDA [21], orthogonal LDA (OLDA) [62], uncorrelated LDA (ULDA) [63], QR-NLDA Fig.”