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Subspace manifold learning with sample weights
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DOI：10.1016/j.imavis.2006.10.007.png)
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Abstract
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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. (C) 2007 Elsevier B.V. All rights reserved.
Keywords:
Subspace learning
Nonlinear dimensionality reduction
Locally linear embedding
Face recognition
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