Estimating the Intrinsic Dimension of Data with a Fractal-Based Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extended isomap for pattern classification
Eighteenth national conference on Artificial intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Discriminant isometric mapping for face recognition
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Geodesic entropic graphs for dimension and entropy estimation in manifold learning
IEEE Transactions on Signal Processing
Local relative transformation with application to isometric embedding
Pattern Recognition Letters
Adaptive Neighborhood Select Based on Local Linearity for Nonlinear Dimensionality Reduction
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Dynamic Neighborhood Selection for Nonlinear Dimensionality Reduction
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Robust automatic data decomposition using a modified sparse NMF
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
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
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Face recognition using Intrinsicfaces
Pattern Recognition
Automatic configuration of spectral dimensionality reduction methods
Pattern Recognition Letters
Semi-supervised learning for WLAN positioning
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
An effective double-bounded tree-connected Isomap algorithm for microarray data classification
Pattern Recognition Letters
Improved locally linear embedding by cognitive geometry
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Dimensionality reduction with adaptive graph
Frontiers of Computer Science: Selected Publications from Chinese Universities
Isometric sliced inverse regression for nonlinear manifold learning
Statistics and Computing
Orthogonal locally discriminant spline embedding for plant leaf recognition
Computer Vision and Image Understanding
| Hi-index | 0.10 |
The isometric feature mapping (Isomap) method has demonstrated promising results in finding low-dimensional manifolds from data points in high-dimensional input space. Isomap has one free parameter (number of nearest neighbours K or neighbourhood radius @e), which has to be specified manually. In this paper we present a new method for selecting the optimal parameter value for Isomap automatically. Numerous experiments on synthetic and real data sets show the effectiveness of our method.