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Nearest clusters based partial least squares discriminant analysis for the classification of spectral data

Song, Weiran, Wang, Hui, Maguire, Paul and Nibouche, Omar (2018) Nearest clusters based partial least squares discriminant analysis for the classification of spectral data. Analytica Chimica Acta, 1009 . pp. 27-38. [Journal article]

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URL: https://www.sciencedirect.com/science/article/pii/S0003267018300886?via%3Dihub

DOI: 10.1016/j.aca.2018.01.023


Partial Least Squares Discriminant Analysis (PLS-DA) is one of the most effective multivariate analysis methods for spectral data analysis, which extracts latent variables and uses them to predict responses. In particular, it is an effective method for handling high-dimensional and collinear spectral data. However, PLS-DA does not explicitly address data multimodality, i.e., within-class multimodal distribution of data. In this paper, we present a novel method termed nearest clusters based PLS-DA (NCPLS-DA) for addressing the multimodality and nonlinearity issues explicitly and improving the performance of PLS-DA on spectral data classification. The new method applies hierarchical clustering to divide samples into clusters and calculates the corresponding centre of every cluster. For a given query point, only clusters whose centres are nearest to such a query point are used for PLS-DA. Such a method can provide a simple and effective tool for separating multimodal and nonlinear classes into clusters which are locally linear and unimodal. Experimental results on 17 datasets, including 12 UCI and 5 spectral datasets, show that NCPLS-DA can outperform 4 baseline methods, namely, PLS-DA, kernel PLS-DA, local PLS-DA and k-NN, achieving the highest classification accuracy most of the time.

Item Type:Journal article
Keywords:Partial Least Squares, Clustering, Nonlinearity, Multimodality, Spectral pattern recognition.
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > School of Engineering
Research Institutes and Groups:Engineering Research Institute
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:39601
Deposited By: Dr Omar Nibouche
Deposited On:22 Feb 2018 15:00
Last Modified:22 Feb 2018 15:00

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