Various methods for multivariate calibration like Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) are evaluated for their use in the field of pattern classification. These methods have the advantage that they can deal with high-dimensional feature spaces and multi-collinear data, since they inherently reduce the dimension of the feature space to represent it by one single dimension. Additionally, they yield very simple linear classifiers, which can be used for real-time calculation. These properties make the methods particularly useful in the field of image processing, where one often find high-dimensional spaces with linearly dependent data and usually we have tight requirements on computational complexity.

Date Published: 18 October 2002
PDF: 7 pages
Proc. SPIE 4902, Optomechatronic Systems III, (18 October 2002); doi: 10.1117/12.467254

Published in SPIE Proceedings Vol. 4902:
Optomechatronic Systems III
Toru Yoshizawa, Editor(s)