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Abstract. In this paper we introduce a new error measure, integrated reconstruction error (IRE) and show that the minimization of IRE leads to principal eigenvectors (without rotat...
When performing subspace modelling of data using Principal Component Analysis (PCA) it may be desirable to constrain certain directions to be more meaningful in the context of the...
An analysis is presented that extends existing Rayleigh-Ritz theory to the special case of highly eccentric distributions. Specifically, a bound on the angle between the first Rit...
Abstract: The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like ...
A method is proposed for the compression of hyperspectral signature vectors on severely resourceconstrained encoding platforms. The proposed technique, compressive-projection prin...