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Dimensionality reduction with image data

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  • Benito Bonito, Mónica
  • Peña, Daniel

Abstract

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.

Suggested Citation

  • Benito Bonito, Mónica & Peña, Daniel, 2004. "Dimensionality reduction with image data," DES - Working Papers. Statistics and Econometrics. WS ws041003, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws041003
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    File URL: https://e-archivo.uc3m.es/bitstream/handle/10016/207/ws041003.pdf?sequence=1
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    References listed on IDEAS

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    1. C. M. Theobald & C. A. Glasbey & G. W. Horgan & C. D. Robinson, 2004. "Principal component analysis of landmarks from reversible images," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(1), pages 163-175, January.
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    Cited by:

    1. Giuliodori, Andrea & Lillo Rodríguez, Rosa Elvira & Peña, Daniel, 2009. "Clustering and classifying images with local and global variability," DES - Working Papers. Statistics and Econometrics. WS ws090101, Universidad Carlos III de Madrid. Departamento de Estadística.

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