Subspace manifold learning with sample weights

Nathan Mekuz, Christian Bauckhage, John K. Tsotsos
2009 Image and Vision Computing  
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
more » ... 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.
doi:10.1016/j.imavis.2006.10.007 fatcat:vf2lgklcivaqxn4kelcnfivq4e