Generalizing discriminant analysis using the generalized singular value decomposition

By Peg Howland, Haesun Park. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004

374 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c197f935f4e94ec46f6295b06d28ab9423df3b33

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. R. Strauss
2. Jiawei Han
3. Deng Cai
4. Xiaofei He
5. Chandan K. Reddy
6. Wei Peng
7. Charu C. Aggarwal
8. Chengxiang Zhai

In Computer Vision and Pattern Recognition

1. Deng Cai
2. Jiawei Han
3. Charu C. Aggarwal
4. Xiaofei He
5. Shuicheng Yan
6. Dacheng Tao
7. Haesun Park
8. Chris H. Q. Ding

In Statistics and Probability

1. Jiawei Han
2. Deng Cai
3. Xiaofei He
4. R. Strauss
5. Charu C. Aggarwal
6. Shuicheng Yan
7. Chandan K. Reddy
8. Wei Peng

Citations in the corpus

• UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization (2013, methodology): “To overcome this problem, we adopted a supervision idea in dimension reduction, such as linear discriminant analysis [19], which tries to represent the clear cluster structure in dimension reduction results.”
• Trace optimization and eigenproblems in dimension reduction methods (2011, background): “Incidentally, the above problem can also be formulated as a generalized singular value problem (see e.g., [23]).”
• A Survey of Text Classification Algorithms (2012, result): “Interestingly, these different lines of work have also resulted in a number of similar conclusions in terms of the effectiveness of the different classifiers.”
• Discriminating Groups of Organisms (2010, methodology): “Some recently developed methods for performing discriminant analysis with relatively small sample sizes (e.g. Anderson and Robinson 2003; Howland and Park 2004; Ye et al. 2004) seem promising, but none have yet been applied to morphometric data.”
• SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis (2008, methodology): “, two-stage PCA+LDA approach [1] and LDA/GSVD approaches [16][25], can be unified in a SVD+LDA framework.”
• Orthogonal nonnegative matrix t-factorizations for clustering (2006, methodology): “Others include Latent Semantic Indexing [1], scaled PCA [10], generalized SVD [28], etc.”
• 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].”