1 February 1992 Fast approximate factor analysis
Author Affiliations +
Abstract
The principal orthogonal factor analysis or Karhunen-Loeve algorithm may be sped up by a low-complexity preprocessing step. A fast transform is selected from a large library of wavelet-like orthonormal bases, so as to maximize transform coding gain for an ensemble of vectors. Only the top few coefficients in the new basis, in order of variance across the ensemble, are then decorrelated by diagonalizing the autocovariance matrix. The method has computational complexity O(d2 log d + d'3) and O(d log d + d'2) respectively for training and classifying a d-dimensional system, where d' << d. One application is described, the reduction of an ensemble of 16,384 pixel face images to a 10 parameter space using a desktop computer, retaining 90% of the variance of the ensemble.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mladen Victor Wickerhauser, "Fast approximate factor analysis", Proc. SPIE 1610, Curves and Surfaces in Computer Vision and Graphics II, (1 February 1992); doi: 10.1117/12.135163; https://doi.org/10.1117/12.135163
PROCEEDINGS
10 PAGES


SHARE
RELATED CONTENT

Rendering of NURBS curves (Invited Paper)
Proceedings of SPIE (February 01 1992)
Surface reconstruction with discontinuities
Proceedings of SPIE (February 01 1992)
Corner detection using spline wavelets
Proceedings of SPIE (February 01 1992)
Bezier representation for cubic surface patches
Proceedings of SPIE (February 01 1992)
Image smoothing and differentiation with regularized filters
Proceedings of SPIE (February 01 1992)

Back to Top