Self-organizing maps
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A GA-based RBF classifier with class-dependent features
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Using graph algebra to optimize neighborhood for isometric mapping
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Clustering-based nonlinear dimensionality reduction on manifold
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Performing locally linear embedding with adaptable neighborhood size on manifold
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
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Locally Linear Embedding (LLE) is an efficient nonlinear algorithm for mapping high-dimensional data to a low-dimensional observed space. However, the algorithm is sensitive to several parameters that should be set artificially, and the resulting maps may be invalid in case of noises. In this paper, the original LLE algorithm is improved by introducing the self-organizing features of a novel SOM model we proposed recently called DGSOM to overcome these shortages. In the improved algorithm, nearest neighbors are selected automatically according to the topology connections derived from DGSOM. The proposed algorithm can also estimate the intrinsic dimensionality of the manifold and eliminate noises simultaneously. All these advantages are illustrated with abundant experiments and simulations.