FD-5: Multivariate Analysis of Imaging Data

Presented by: Peter Bajorski
Rochester Institute of Technology, USA

In this course, the participants will learn useful tools for the analysis of data on many variables (such as data on many spectral bands or on several responses observed in an experiment). They will identify the benefits of incorporating information from several variables as opposed to analyzing each variable separately. Through understanding the principles behind the analytical tools, the participants will be able to decide when these tools should or should not be used in practice. Many practical and useful examples of analyses of imaging data are included. The instructor will emphasize intuitive and geometric understanding of the introduced concepts. The topics covered include principal component analysis (PCA), canonical correlation analysis, discrimination and classification (supervised learning), Fisher discrimination, and independent component analysis (ICA).

At the end of this course, the participants will be able to

List of topics:

This course is intended for participants who want to gain better insight into their multivariate data. Participants are expected to have a basic knowledge of vector and matrix algebra as well as some basic univariate statistics.

Peter Bajorski is an Associate Professor of Statistics at Rochester Institute of Technology, Rochester, NY, USA. Previously, he held positions at Cornell University, the University of British Columbia, and Simon Fraser University. He received the B.S./M.S. degrees in mathematics and the Ph.D. degree in mathematical statistics. He teaches graduate courses in statistics including a course on Multivariate Statistics for Imaging Science. He also designs and teaches short courses in industry, with longer-term follow-up and consulting. He has published over 50 research papers in statistics and in hyperspectral imaging. Dr. Bajorski is past-president of the Rochester Chapter of the American Statistical Association. He is also a senior member of SPIE and a senior member of IEEE. His book on Statistics for Imaging, Optics, and Photonics was published in the prestigious Wiley Series in Probability and Statistics in 2011.