NDLI: Linear Principal Component Discriminant Analysis

Content Provider	IEEE Xplore Digital Library
Author	Yan Pei
Copyright Year	2015
Description	Author affiliation: Comput. Sci. Div., Univ. of Aizu, Aizu-Wakamatsu, Japan (Yan Pei)
Abstract	We propose a series of data analysis methods for both supervised and un-supervised learning techniques. Three objectives of data relationship and characteristics are used to establish a uniform framework of our proposed methods, which are inspired by principal component analysis and linear discriminant analysis. By using the three objectives and some combinations of them, we investigate and illustrate the performance of the proposed methods. We use simulation data and classical Iris data to investigate the proposed methods. Some discoveries and issues are analysed and discussed arising from the evaluation results. The advantages of the proposed framework do not only depend on its explanation capability of data relationship, but also depend on the fusion of multiple data projection techniques. We investigate some potential research issues of the proposed methods. Some works which extend the current study with kernel method are analysed theoretically. We also present some characteristics of the proposal and discuss some open opportunities and future works.
Starting Page	2108
Ending Page	2113
File Size	354469
Page Count	6
File Format	PDF
e-ISBN	9781479986972
DOI	10.1109/SMC.2015.368
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Iris Data analysis Proposals Eigenvalues and eigenfunctions Feature extraction Kernel Kernel method Principal component discriminate analysis Linear discriminate analysis Multivariate analysis
Content Type	Text
Resource Type	Article

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