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
Head Pose Estimation by Nonlinear Manifold Learning
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Graph Embedded Analysis for Head Pose Estimation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Robust Head Pose Estimation Using LGBP
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Synchronized submanifold embedding for person-independent pose estimation and beyond
IEEE Transactions on Image Processing
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
On Appearance Based Face and Facial Action Tracking
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose a parameterless Local Discriminant Embedding. Recently, local discriminant embedding (LDE) method was proposed in order to tackle some limitations of the global linear discriminant analysis (LDA) method. LDE splits the graph Laplacian into two components: within-class adjacency graph and between-class adjacency graph to better characterize the discriminant property of the data. However, it is very difficult to set in advance the within- and between-class graphs. Our proposed LDE variant has two important characteristics: (i) while all graph-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. The resulting revisited LDE approach has been applied to the problem of model-less coarse 3D head pose estimation (person independent 3D pose estimation). It was tested on two large 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.