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
Improved locally linear embedding by cognitive geometry
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
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Locally linear embedding is a nonlinear method for dimensionality reduction and manifold learning. It requires well-sampled input data in high dimensional space so that neighborhoods of all data points overlap with each other. In this paper, we build connected neighborhood graphs for the purpose of assigning neighbor points. A few methods are examined to build connected neighborhood graphs. They have made LLE applicable to a wide range of data including under-sampled data and non-uniformly distributed data. These methods are compared through experiments on both synthetic and real world data sets.