Sciweavers

Share
SGAI
2005
Springer

The Effect of Principal Component Analysis on Machine Learning Accuracy with High Dimensional Spectral Data

10 years 6 months ago
The Effect of Principal Component Analysis on Machine Learning Accuracy with High Dimensional Spectral Data
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 e...
Tom Howley, Michael G. Madden, Marie-Louise O'Conn
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where SGAI
Authors Tom Howley, Michael G. Madden, Marie-Louise O'Connell, Alan G. Ryder
Comments (0)
books