A two-stage linear discriminant analysis via QR-decomposition

By Jieping Ye, Qi Li. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

299 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/187e1ac48c318cb84f519f662f0d09a98ea2306e

How researchers see this paper

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

In Analytical Chemistry

1. Shuicheng Yan
2. Saurabh Prasad
3. Jian Yang

In Computer Vision and Pattern Recognition

1. Weihong Deng
2. Lei Zhang
3. Jiani Hu
4. Jian Yang
5. Shuicheng Yan
6. Jun Guo
7. Honggang Zhang
8. Chuang Zhang

Citations in the corpus

• Geometric Mean for Subspace Selection (2009, unknown): “Ye and Li [42] combined the orthogonal triangular decomposition with FLDA to reduce the SSS problem.”
• Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics (2007, unknown): “Ç”
• Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics" (2007, methodology): “PCA and LDA are two well-known linear subspace learning methods which have been extensively used in pattern recognition and computer vision areas and have become the most popular techniques for face recognition and other biometrics [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]”
• Linear discriminant analysis for the small sample size problem: an overview (2014, unknown): “LDA (CLDA) [8], direct LDA (DLDA) [67], kernel DLDA (KDLDA) [28], parameterized DLDA (PDLDA) [56], improved DLDA (IDLDA) [36], pseudoinverse LDA (PILDA) [59], fast PILDA (FPILDA) [27], improved PILDA (IPILDA) [34], LDA/QR [64], approximate LDA (ALDA) [35], PCA ? LDA [5, 57], regularized LDA (RLDA) [”
• Limitations of Principal Components Analysis for Hyperspectral Target Recognition (2008, background): “One solution that has recently been explored in some pattern classification tasks is subspace LDA (or PCA+LDA) [9], [10].”
• A General Exponential Framework for Dimensionality Reduction (2014, methodology): “To tackle the SSS problem , many variants of LDA have been proposed in the recent years, such as Fisherface [3], null LDA [6], LDA/QR [7], LDA/GSVD [8], LDA/FKT [9], DLA [10], [11], Direct LDA, and its variants [6], [12]–[14].”