A fast approach for dimensionality reduction with image data

Authors:
Mónica Benito;Daniel Peña
Affiliations:
Department of Statistics, Universidad Carlos III de Madrid, Spain;Department of Statistics, Universidad Carlos III de Madrid, Spain
Venue:
Pattern Recognition
Year:
2005

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Abstract

An important objective in image analysis is dimensionality reduction. The most often used data-exploratory technique with this objective is principal component analysis, which performs a singular value decomposition on a data matrix of vectorized images. When considering an array data or tensor instead of a matrix, the high-order generalization of PCA for computing principal components offers multiple ways to decompose tensors orthogonally. As an alternative, we propose a new method based on the projection of the images as matrices and show that it leads to a better reconstruction of images than previous approaches.