The effect of principal component analysis on machine learning accuracy with high-dimensional spectral data

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This paper presents the results of an investigation into the use of machine learning methods for the identification of narcotics from Raman spectra. The classification of spectral data and other high-dimensional data, such as images, gene-expression data and spectral data, poses an interesting challenge to machine learning, as the presence of high numbers of redundant or highly correlated attributes can seriously degrade classification accuracy. This paper investigates the use of principal component analysis (PCA) to reduce high-dimensional spectral data and to improve the predictive performance of some well-known machine learning methods. Experiments are carried out on a high-dimensional spectral dataset. These experiments employ the NIPALS (Non-Linear Iterative Partial Least Squares) PCA method, a method that has been used in the field of chemometrics for spectral classification, and is a more efficient alternative than the widely used eigenvector decomposition approach. The experiments show that the use of this PCA method can improve the performance of machine learning in the classification of high-dimensional data.

论文关键词:Machine learning,High-dimensional data,Principal component analysis,NIPALS,Spectroscopy

论文评审过程:Received 28 October 2005, Accepted 28 November 2005, Available online 8 February 2006.

论文官网地址:https://doi.org/10.1016/j.knosys.2005.11.014