Elements of information theory
Elements of information theory
GTM: the generative topographic mapping
Neural Computation
A local search approximation algorithm for k-means clustering
Proceedings of the eighteenth annual symposium on Computational geometry
Intrinsic Dimension Estimation of Data: An Approach Based on Grassberger–Procaccia's Algorithm
Neural Processing Letters
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Nearest Neighbors in Random Subspaces
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Selection of the optimal parameter value for the Isomap algorithm
Pattern Recognition Letters
Nonlinear manifold learning for dynamic shape and dynamic appearance
Computer Vision and Image Understanding
An Algorithm for Finding Intrinsic Dimensionality of Data
IEEE Transactions on Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
LDR-LLE: LLE with Low-Dimensional Neighborhood Representation
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
IEEE Transactions on Neural Networks
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We propose an advanced framework for the automatic configuration of spectral dimensionality reduction methods. This is achieved by introducing, first, the mutual information measure to assess the quality of discovered embedded spaces. Secondly, unsupervised Radial Basis Function network is designated for mapping between spaces where the learning process is derived from graph theory and based on Markov cluster algorithm. Experiments on synthetic and real datasets demonstrate the effectiveness of the proposed methodology.