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Paper details
Number 1 - March 2014
Volume 24 - 2014
An efficient eigenspace updating scheme for high-dimensional systems
Simon Gangl, Domen Mongus, Borut Žalik
Abstract
Systems based on principal component analysis have developed from exploratory data analysis in the past to current data
processing applications which encode and decode vectors of data using a changing projection space (eigenspace). Linear
systems, which need to be solved to obtain a constantly updated eigenspace, have increased significantly in their dimensions
during this evolution. The basic scheme used for updating the eigenspace, however, has remained basically the same:
(re)computing the eigenspace whenever the error exceeds a predefined threshold. In this paper we propose a computationally
efficient eigenspace updating scheme, which specifically supports high-dimensional systems from any domain. The
key principle is a prior selection of the vectors used to update the eigenspace in combination with an optimized eigenspace
computation. The presented theoretical analysis proves the superior reconstruction capability of the introduced scheme,
and further provides an estimate of the achievable compression ratios.
Keywords
eigenspace updating, projection space, data compression, principal component analysis