Dimensionality Reduction with Image Data

A common objective in image analysis is dimensionality reduction. The most common often used data-exploratory technique with this objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a Procrustes rotation and show that it leads to a better reconstruction of images.

• Type: Conference
• Year: 2004
• Where: IDEAL
• Authors: Mónica Benito, Daniel Peña
• Keywords: Common Objective, Dimensionality Reduction, IDEAL 2004, Principal Component Analysis