@article{a7b7a48dbc174986a3ff6736ec345ed1,
title = "Sparse Orthogonal Linear Discriminant Analysis",
abstract = "In this paper, sparse orthogonal linear discriminant analysis (OLDA) is studied. The main contributions of the present work include the following: (i) all minimum Frobenius-norm/dimension solutions of the optimization problem used for establishing OLDA are characterized explicitly; and (ii) this explicit characterization leads to two numerical algorithms for computing a sparse linear transformation for OLDA. The first is based on the gradient flow approach while the second is a sequential linear Bregman method. We experiment with real world datasets to illustrate that the sequential linear Bregman method is much better than the gradient flow approach. The sequential linear Bregman method always achieves comparable classification accuracy with the normal OLDA, satisfactory sparsity and orthogonality, and acceptable CPU times.",
keywords = "Dimensionality reduction, Linear discriminant analysis, Sparsity",
author = "Delin Chu and Liao, {Li Zhi} and Ng, {Michael K.}",
note = "Funding information: Department of Mathematics, National University of Singapore, 119076, Singapore (matchudl@ nus.edu.sg). This author{\textquoteright}s work was supported in part by NUS Research grant R-146-000-140-112. Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong (
[email protected]). This author{\textquoteright}s work was supported in part by GRF grants HKBU201409 and HKBU201611 from the Research Grant Council of Hong Kong. Center for Mathematical Imaging and Vision, and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong (
[email protected]). This author{\textquoteright}s work was supported in part by grants from Hong Kong Baptist Univers ity (FRG) and the Research Grant Council of Hong Kong HKBU201812. Publisher copyright: {\textcopyright} 2012, Society for Industrial and Applied Mathematics",
year = "2012",
month = sep,
day = "5",
doi = "10.1137/110851377",
language = "English",
volume = "34",
pages = "A2421--A2443",
journal = "SIAM Journal on Scientific Computing",
issn = "1064-8275",
publisher = "Society for Industrial and Applied Mathematics (SIAM)",
number = "5",
}