The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
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
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Robust locally linear embedding
Pattern Recognition
Improving nearest neighbor classification with cam weighted distance
Pattern Recognition
Rapid and Brief communication: Formulating LLE using alignment technique
Pattern Recognition
Rapid and brief communication: The LLE and a linear mapping
Pattern Recognition
Face detection in gray scale images using locally linear embeddings
Computer Vision and Image Understanding
Letters: ISOLLE: LLE with geodesic distance
Neurocomputing
Probability-Based Locally Linear Embedding for Classification
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Noisy manifold learning using neighborhood smoothing embedding
Pattern Recognition Letters
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
Neighbourhood Discriminant Locally Linear Embedding in Face Recognition
CGIV '08 Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation
Local Linear Embedding in Dimensionality Reduction Based on Small World Principle
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
Robust and Stable Locally Linear Embedding
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Generalized Locally Linear Embedding Based on Local Reconstruction Similarity
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 05
LDR-LLE: LLE with Low-Dimensional Neighborhood Representation
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
An Efficient Algorithm of Learning the Parametric Map of Locally Linear Embedding
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 03
Weighted locally linear embedding for dimension reduction
Pattern Recognition
Supervised locally linear embedding with probability-based distance for classification
Computers & Mathematics with Applications
Local linear transformation embedding
Neurocomputing
Stable local dimensionality reduction approaches
Pattern Recognition
Robust-SL0 for stable sparse representation in noisy settings
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Enhanced supervised locally linear embedding
Pattern Recognition Letters
k/K-Nearest Neighborhood Criterion for Improvement of Locally Linear Embedding
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Complete neighborhood preserving embedding for face recognition
Pattern Recognition
Automatic Choice of the Number of Nearest Neighbors in Locally Linear Embedding
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Nonlinear embedding preserving multiple local-linearities
Pattern Recognition
Rapid and brief communication: Incremental locally linear embedding
Pattern Recognition
A hand gesture recognition system based on local linear embedding
Journal of Visual Languages and Computing
Performing locally linear embedding with adaptable neighborhood size on manifold
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Efficient locally linear embeddings of imperfect manifolds
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A more topologically stable locally linear embedding algorithm based on R*-tree
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Local smoothing for manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Neighbor line-based locally linear embedding
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Improved locally linear embedding through new distance computing
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
As a classic method of nonlinear dimensional reduction, locally linear embedding (LLE) is more and more attractive to researchers due to its ability to deal with large amounts of high dimensional data and its non-iterative way of finding the embeddings. However, several problems in the LLE algorithm still remain open, such as its sensitivity to noise, inevitable ill-conditioned eigenproblems, the lack of how to deal with the novel data, etc. The existing extensions are comprehensively reviewed and discussed classifying into different categories in this paper. Their strategies, advantages/disadvantages and performances are elaborated. By generalizing different tactics in various extensions related to different stages of LLE and evaluating their performances, several promising directions for future research have been suggested.