Incremental Nonlinear Dimensionality Reduction by Manifold Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A facial expression recognition system based on supervised locally linear embedding
Pattern Recognition Letters
SMVLLE: An Efficient Dimension Reduction Scheme
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Neighbourhood preserving discriminant embedding in face recognition
Journal of Visual Communication and Image Representation
Efficient locally linear embeddings of imperfect manifolds
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Coarse head pose estimation of construction equipment operators to formulate dynamic blind spots
Advanced Engineering Informatics
Hi-index | 0.00 |
This paper considers a recently proposed method for unsupervised learning and dimensionalityreduction, locally linear embedding (LLE). LLE computes a compact representation of high-dimensional data combining the major advantages of linear methods (computational efficiency, global optimality, and flexible asymptotic convergence guarantees) with the advantages of non-linear approaches (flexibility to learn a broad of class on non-linear manifolds). We assess the performance of the LLE algorithm on a real-world data (face imagesin different poses) and compare the results with those obtained with two different approaches (PCA and SOM). Extensions to the original LLE algorithm are proposed and applied to the problem of pose estimation.