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ECCV
2002
Springer

Principal Component Analysis over Continuous Subspaces and Intersection of Half-Spaces

11 years 6 months ago
Principal Component Analysis over Continuous Subspaces and Intersection of Half-Spaces
Abstract. Principal Component Analysis (PCA) is one of the most popular techniques for dimensionality reduction of multivariate data points with application areas covering many branches of science. However, conventional PCA handles the multivariate data in a discrete manner only, i.e., the covariance matrix represents only sample data points rather than higher-order data representations. In this paper we extend conventional PCA by proposing techniques for constructing the covariance matrix of uniformly sampled continuous regions in parameter space. These regions include polytops de ned by convex combinations of sample data, and polyhedral regions de ned by intersection of half spaces. The applications of these ideas in practice are simple and shown to be very e ective in providing much superior generalization properties than conventional PCA for appearance-based recognition applications.
Anat Levin, Amnon Shashua
Added 16 Oct 2009
Updated 16 Oct 2009
Type Conference
Year 2002
Where ECCV
Authors Anat Levin, Amnon Shashua
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