Sr-LDA:Sparse and Reduced-Rank Linear Discriminant Analysis for High Dimensional Matrix
Author:
Affiliation:
1. School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
2. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
Funder
National Natural Science Foundation of China
NSF of Shanghai
Fundamental Research Funds for the Central Universities
HKPolyU
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/97/10380231/10496182.pdf?arnumber=10496182
Reference29 articles.
1. Regression Shrinkage and Selection Via the Lasso
2. Sparse linear discriminant analysis by thresholding for high dimensional data
3. A direct approach to sparse discriminant analysis in ultra-high dimensions
4. Multiple rank multi-linear SVM for matrix data classification
5. Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network
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