Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
A facial expression recognition system based on supervised locally linear embedding
Pattern Recognition Letters
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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The locally linear embedding (LLE) algorithm is an unsupervised technique for nonlinear dimensionality reduction which can represent the underlying manifold as well as possible. While in classification, data label information is available and our main purpose changes to represent class separability as well as possible. To the end of classification, we propose a new supervised variant of LLE, called orthogonal centroid locally linear embedding (OCLLE) algorithm in this paper. It uses class membership information to map overlapping high-dimensional data into disjoint clusters in the embedded space. Experiments show that very promising results are yielded by this variant.