Proceedings of the IEEK Conference (대한전자공학회:학술대회논문집)
- 2002.07a
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- Pages.698-701
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- 2002
SEQUENTIAL EM LEARNING FOR SUBSPACE ANALYSIS
Abstract
Subspace analysis (which includes PCA) seeks for feature subspace (which corresponds to the eigenspace), given multivariate input data and has been widely used in computer vision and pattern recognition. Typically data space belongs to very high dimension, but only a few principal components need to be extracted. In this paper I present a fast sequential algorithm for subspace analysis or tracking. Useful behavior of the algorithm is confirmed by numerical experiments.
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