TY - JOUR
T1 - Generalising combinatorial discriminant analysis through conditioning truncated Rayleigh flow
AU - Yang, Sijia
AU - Xiong, Haoyi
AU - Hu, Di
AU - Xu, Kaibo
AU - Wang, Licheng
AU - Zhu, Peizhen
AU - Sun, Zeyi
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
PY - 2021/8
Y1 - 2021/8
N2 - Fisher’s Linear Discriminant Analysis (LDA) has been widely used for linear classification, feature selection, and metrics learning in multivariate data analytics. To ensure high classification accuracy while optimally discovering predictive features from the data, this paper studied CDA, namely Combinatorial Discriminant Analysis that intends to combinatorially select a subset of features and assign weights to them optimally. CDA extents the Truncated Rayleigh Flow algorithm (Tan et al. in J R Stat Soc: Ser B (Stat Methodol) 80(5):1057–1086, 2018) and improves LDA estimation under k-sparsity constraint. The experimental results based on the synthesized and real-world datasets demonstrate that our algorithm outperforms other LDA baselines and downstream classifiers. The empirical analysis shows that our algorithm can recover the combinatorial structure of optimal LDA with empirical consistency.
AB - Fisher’s Linear Discriminant Analysis (LDA) has been widely used for linear classification, feature selection, and metrics learning in multivariate data analytics. To ensure high classification accuracy while optimally discovering predictive features from the data, this paper studied CDA, namely Combinatorial Discriminant Analysis that intends to combinatorially select a subset of features and assign weights to them optimally. CDA extents the Truncated Rayleigh Flow algorithm (Tan et al. in J R Stat Soc: Ser B (Stat Methodol) 80(5):1057–1086, 2018) and improves LDA estimation under k-sparsity constraint. The experimental results based on the synthesized and real-world datasets demonstrate that our algorithm outperforms other LDA baselines and downstream classifiers. The empirical analysis shows that our algorithm can recover the combinatorial structure of optimal LDA with empirical consistency.
UR - https://www.scopus.com/pages/publications/85110369146
U2 - 10.1007/s10115-021-01587-z
DO - 10.1007/s10115-021-01587-z
M3 - Article
AN - SCOPUS:85110369146
SN - 0219-1377
VL - 63
SP - 2189
EP - 2208
JO - Knowledge and Information Systems
JF - Knowledge and Information Systems
IS - 8
ER -