Robust regression and outlier detection
Robust regression and outlier detection
Bumptrees for efficient function, constraint, and classification learning
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Similarity metric learning for a variable-kernel classifier
Neural Computation
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Jacobi--Davidson Style QR and QZ Algorithms for the Reduction of Matrix Pencils
SIAM Journal on Scientific Computing
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition with Weighted Locally Linear Embedding
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Improving nearest neighbor classification with cam weighted distance
Pattern Recognition
RBF-based neurodynamic nearest neighbor classification in real pattern space
Pattern Recognition
Distance Learning for Similarity Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood linear embedding for intrinsic structure discovery
Machine Vision and Applications
An Optimal Global Nearest Neighbor Metric
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-Euclidean or non-metric measures can be informative
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Optimization of k nearest neighbor density estimates
IEEE Transactions on Information Theory
The optimal distance measure for nearest neighbor classification
IEEE Transactions on Information Theory
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Enhanced supervised locally linear embedding
Pattern Recognition Letters
Face Recognition Using ALLE and SIFT for Human Robot Interaction
Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics
Equivalent Relationship of Feedforward Neural Networks and Real-Time Face Detection System
Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics
Nonlinear embedding preserving multiple local-linearities
Pattern Recognition
Outlier-resisting graph embedding
Neurocomputing
Orthogonal local spline discriminant projection with application to face recognition
Pattern Recognition Letters
Locally linear embedding: a survey
Artificial Intelligence Review
Global and local choice of the number of nearest neighbors in locally linear embedding
Pattern Recognition Letters
Geometrically local embedding in manifolds for dimension reduction
Pattern Recognition
Information Processing and Management: an International Journal
Neighborhood selection and eigenvalues for embedding data complex in low dimension
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Stability of dimensionality reduction methods applied on artificial hyperspectral images
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
A comparative study of nonlinear manifold learning methods for cancer microarray data classification
Expert Systems with Applications: An International Journal
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The low-dimensional representation of high-dimensional data and the concise description of its intrinsic structures are central problems in data analysis. In this paper, an unsupervised learning algorithm called weighted locally linear embedding (WLLE) is presented to discover the intrinsic structures of data, such as neighborhood relationships, global distributions and clustering. The WLLE algorithm is motivated by locally linear embedding (LLE) algorithm and cam weighted distance, a novel distance measure which usually gives a deflective cam contours for equal-distance contour in classification for an improved classification. It is a major advantage of the WLLE to optimize the process of intrinsic structure discovery by avoiding unreasonable neighbor searching, and at the same time, allow the discovery adapt to the characteristics of input data set. Furthermore, the algorithm discovers intrinsic structures which can be used to compute manipulative embedding for potential classification and recognition purposes, thus can work as a feature extraction algorithm. Simulation studies demonstrate that the WLLE can give better results in manifold learning and dimension reduction than LLE and neighborhood linear embedding (NLE), and is more robust to parameter changes. Experiments on face images data sets and comparison to other famous face recognition methods such as kernel-PCA (KPCA) and kernel direct discriminant analysis (KDDA) are done to show the potential of WLLE for real world problem.