Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Distance metric learning vs. Fisher discriminant analysis
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On Appearance Based Face and Facial Action Tracking
IEEE Transactions on Circuits and Systems for Video Technology
Parameterless Local Discriminant Embedding
Neural Processing Letters
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In this paper we propose a novel parameterless approach for discriminative analysis. By following the large margin concept, the graph Laplacian is split in two components: within-class graph and betweenclass graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) while all spectralgraph based manifold learning techniques (supervised and unsupervised) are depending on several parameters that require manual tuning, ours is parameter-free, and (ii) it adaptively estimates the local neighborhood surrounding each sample based on the data similarity. Our approach has been applied to the problem of modeless coarse 3D head pose estimation. It was tested on two databases FacePix and Pointing'04. It was conveniently compared with other linear techniques. The experimental results confirm that our method outperforms, in general, the existing ones. Although we have concentrated in this paper on coarse 3D head pose problem, the proposed approach could also be applied to other classification tasks for objects characterized by large variance in their appearance.