典型文献
Sparse linear discriminant analysis via lo constraint
文献摘要：
We consider the problem of interpretable classification in a high-dimensional setting,where the number of fea-tures is extremely large and the number of observations is limited.This setting has been extensively studied in the chemo-metric literature and has recently become pervasive in the biological and medical literature.Linear discriminant analysis(LDA)is a canonical approach for solving this problem.However,in the case of high dimensions,LDA is unsuitable for two reasons.First,the standard estimate of the within-class covariance matrix is singular;therefore,the usual discriminant rule cannot be applied.Second,when p is large,it is difficult to interpret the classification rules obtained from LDA be-cause p features are involved.In this setting,motivated by the success of the primal-dual active set algorithm for best sub-set selection,we propose a method for sparse linear discriminant analysis via lo constraint,which imposes a sparsity cri-terion when performing linear discriminant analysis,allowing classification and feature selection to be performed simul-taneously.Numerical results on synthetic and real data suggest that our method obtains competitive results compared with existing alternative methods.
文献关键词：
中图分类号：
作者姓名：
Qi Yin;Lei Shu
作者机构：
Department of Statistics and Finance,School of Management,University of Science and Technology of China,Hefei 230026,China
文献出处：
引用格式：
[1]Qi Yin;Lei Shu-.Sparse linear discriminant analysis via lo constraint)[J].中国科学技术大学学报,2022(08):19-25
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B类：
Sparse,linear,discriminant,analysis,via,constraint,We,consider,problem,interpretable,classification,high,dimensional,setting,where,number,extremely,large,observations,limited,This,has,been,extensively,studied,chemo,metric,literature,recently,become,pervasive,biological,medical,Linear,LDA,canonical,approach,solving,this,However,case,dimensions,unsuitable,two,reasons,First,standard,estimate,within,covariance,matrix,singular,therefore,usual,cannot,applied,Second,when,difficult,rules,obtained,from,cause,features,involved,In,motivated,by,success,primal,dual,active,algorithm,best,sub,selection,propose,sparse,which,imposes,sparsity,terion,performing,allowing,performed,simul,taneously,Numerical,results,synthetic,real,data,suggest,that,our,obtains,competitive,compared,existing,alternative,methods
AB值：
0.611686
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