Collaborative representation based classifier with partial least squares regression for the classification of spectral data

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2 Citations (Scopus)

Abstract

The need for effective methods to classify high-dimensional spectral data is increasing in tasks such as rapid and non-destructive detection of object features and chemical species using spectroscopy. Partial least squares discriminant analysis (PLS-DA) is an effective, multivariate regression based method for spectral data classification. Although powerful, PLS-DA suffers from performance degradation under complex conditions such as nonlinearity, class imbalance and multiclass, which are common in real-world applications. Collaborative representation-based classifier (CRC) is a new machine learning algorithm which represents a query by a linear combination of training samples and classifies the query based on the representation. It offers the possibility of good classification performance even under nonlinearity, class imbalance and multiclass conditions. In this paper, we present a novel method for spectral data classification, namely CRC-WPLS, which reaps the benefits of both PLS regression and CRC. This method searches for a weighted, linear combination of all training samples to represent the query by using PLS regression, and then assigns the query to the class which yields the least approximation error. CRC-WPLS is compared to PLS-DA, kernel PLS-DA, support vector machine (SVM), random forest (RF) and representation-based classifiers on fourteen general machine learning datasets and three spectral datasets. Experimental results show the proposed method can outperform 7 baseline methods in most cases, and achieve a high classification accuracy (>90%) for low grade spectra obtained from portable instrumentation.
LanguageEnglish
Pages79-86
Number of pages8
JournalChemometrics and Intelligent Laboratory Systems
Volume182
Early online date21 Aug 2018
DOIs
Publication statusE-pub ahead of print - 21 Aug 2018

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Discriminant analysis
Classifiers
Learning systems
Learning algorithms
Support vector machines
Spectroscopy
Degradation

Keywords

  • Classification
  • Partial least squares
  • Collaborative representation
  • Spectral data

Cite this

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title = "Collaborative representation based classifier with partial least squares regression for the classification of spectral data",
abstract = "The need for effective methods to classify high-dimensional spectral data is increasing in tasks such as rapid and non-destructive detection of object features and chemical species using spectroscopy. Partial least squares discriminant analysis (PLS-DA) is an effective, multivariate regression based method for spectral data classification. Although powerful, PLS-DA suffers from performance degradation under complex conditions such as nonlinearity, class imbalance and multiclass, which are common in real-world applications. Collaborative representation-based classifier (CRC) is a new machine learning algorithm which represents a query by a linear combination of training samples and classifies the query based on the representation. It offers the possibility of good classification performance even under nonlinearity, class imbalance and multiclass conditions. In this paper, we present a novel method for spectral data classification, namely CRC-WPLS, which reaps the benefits of both PLS regression and CRC. This method searches for a weighted, linear combination of all training samples to represent the query by using PLS regression, and then assigns the query to the class which yields the least approximation error. CRC-WPLS is compared to PLS-DA, kernel PLS-DA, support vector machine (SVM), random forest (RF) and representation-based classifiers on fourteen general machine learning datasets and three spectral datasets. Experimental results show the proposed method can outperform 7 baseline methods in most cases, and achieve a high classification accuracy (>90{\%}) for low grade spectra obtained from portable instrumentation.",
keywords = "Classification, Partial least squares, Collaborative representation, Spectral data",
author = "Weiran Song and H. Wang and P Maguire and O Nibouche",
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N2 - The need for effective methods to classify high-dimensional spectral data is increasing in tasks such as rapid and non-destructive detection of object features and chemical species using spectroscopy. Partial least squares discriminant analysis (PLS-DA) is an effective, multivariate regression based method for spectral data classification. Although powerful, PLS-DA suffers from performance degradation under complex conditions such as nonlinearity, class imbalance and multiclass, which are common in real-world applications. Collaborative representation-based classifier (CRC) is a new machine learning algorithm which represents a query by a linear combination of training samples and classifies the query based on the representation. It offers the possibility of good classification performance even under nonlinearity, class imbalance and multiclass conditions. In this paper, we present a novel method for spectral data classification, namely CRC-WPLS, which reaps the benefits of both PLS regression and CRC. This method searches for a weighted, linear combination of all training samples to represent the query by using PLS regression, and then assigns the query to the class which yields the least approximation error. CRC-WPLS is compared to PLS-DA, kernel PLS-DA, support vector machine (SVM), random forest (RF) and representation-based classifiers on fourteen general machine learning datasets and three spectral datasets. Experimental results show the proposed method can outperform 7 baseline methods in most cases, and achieve a high classification accuracy (>90%) for low grade spectra obtained from portable instrumentation.

AB - The need for effective methods to classify high-dimensional spectral data is increasing in tasks such as rapid and non-destructive detection of object features and chemical species using spectroscopy. Partial least squares discriminant analysis (PLS-DA) is an effective, multivariate regression based method for spectral data classification. Although powerful, PLS-DA suffers from performance degradation under complex conditions such as nonlinearity, class imbalance and multiclass, which are common in real-world applications. Collaborative representation-based classifier (CRC) is a new machine learning algorithm which represents a query by a linear combination of training samples and classifies the query based on the representation. It offers the possibility of good classification performance even under nonlinearity, class imbalance and multiclass conditions. In this paper, we present a novel method for spectral data classification, namely CRC-WPLS, which reaps the benefits of both PLS regression and CRC. This method searches for a weighted, linear combination of all training samples to represent the query by using PLS regression, and then assigns the query to the class which yields the least approximation error. CRC-WPLS is compared to PLS-DA, kernel PLS-DA, support vector machine (SVM), random forest (RF) and representation-based classifiers on fourteen general machine learning datasets and three spectral datasets. Experimental results show the proposed method can outperform 7 baseline methods in most cases, and achieve a high classification accuracy (>90%) for low grade spectra obtained from portable instrumentation.

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