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Additional info for Computer Vision — ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28–31, 2002 Proceedings, Part IV
This formula is motivated by the fact that the prior probability of α = αcur + δα directly depends on its norm. The parameter η trades off the prior probability and the fitting quality. Applying singular value decomposition to A yields A = U diag(wi )V T , where wi are the singular values of A. It can be shown that Equation 22 is minimal for: δα = V diag( η wi ) U T δs2d − V diag( 2 ) V T αcur wi2 + η wi + η (23) Note that (i) if η = 0, the update computed by Equation 23 is the same as the one obtained by Equation 18 and that (ii) computing the update using Equation 18 is not much more expensive as computing the update using Equation 23.
Baker, and M. Bsat. The cmu pose, illumination and expression (pie) database of human faces. Technical Report CMU-RI-TR-01-02, CMU, 2000. it Abstract. Learning one class at a time can be seen as an eﬀective solution to classiﬁcation problems in which only the positive examples are easily identiﬁable. A kernel method to accomplish this goal consists of a representation stage - which computes the smallest sphere in feature space enclosing the positive examples - and a classiﬁcation stage - which uses the obtained sphere as a decision surface to determine the positivity of new examples.
N68171-01-C-9000. References 1. Ronen Basri and David Jacobs. Lambertian reflectance and linear subspace. In 8th International Conference on Computer Vision, volume 2, pages 383–390, 2001. 2. R. Bergen and R. Hingorani. Hierarchical motion-based frame rate conversion. Technical report, David Sarnoff Research Center Princeton NJ 08540, 1990. 3. D. Beymer and T. Poggio. Image representation for visual learning. Science, 272, 1996. 4. D. M. Blackburn, M. Bone, and P. J. Phillips. Face recognition vendor test 2000: Evaluation report.