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A review of discriminant analysis in high dimensions
Author(s) -
Mai Qing
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1257
Subject(s) - linear discriminant analysis , computer science , cluster analysis , artificial intelligence , feature selection , classifier (uml) , machine learning , regularization (linguistics) , optimal discriminant analysis , clustering high dimensional data , data mining , data science , pattern recognition (psychology)
Linear discriminant analysis (LDA) is among the most classical classification techniques, while it continues to be a popular and important classifier in practice. However, the advancement of science and technology brings the new challenge of high‐dimensional datasets, where the dimension can be in thousands. In such datasets, LDA is inapplicable. Recently, statisticians have devoted many efforts to creating high‐dimensional LDA methods. These methods typically perform variable selection via regularization techniques. Various theoretical results, algorithms, and empirical results support the application of these methods. In this review, we provide a brief description of difficulties in extending LDA and present some successful proposals. WIREs Comput Stat 2013, 5:190–197. doi: 10.1002/wics.1257 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification