Subspace manifold learning with sample weights

Authors:
Nathan Mekuz;Christian Bauckhage;John K. Tsotsos
Affiliations:
Department of Computer Science and Engineering, Center for Vision Research, York University, CSE 3031, 4700 Keele Street, Toronto, Ont., Canada M3J 1P3;Department of Computer Science and Engineering, Center for Vision Research, York University, CSE 3031, 4700 Keele Street, Toronto, Ont., Canada M3J 1P3;Department of Computer Science and Engineering, Center for Vision Research, York University, CSE 3031, 4700 Keele Street, Toronto, Ont., Canada M3J 1P3
Venue:
Image and Vision Computing
Year:
2009

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Abstract

Subspace manifold learning represents a popular class of techniques in statistical image analysis and object recognition. Recent research in the field has focused on nonlinear representations; locally linear embedding (LLE) is one such technique that has recently gained popularity. We present and apply a generalization of LLE that introduces sample weights. We demonstrate the application of the technique to face recognition, where a model exists to describe each face's probability of occurrence. These probabilities are used as weights in the learning of the low-dimensional face manifold. Results of face recognition using this approach are compared against standard nonweighted LLE and PCA. A significant improvement in recognition rates is realized using weighted LLE on a data set where face occurrences follow the modeled distribution.