Mixtures of probabilistic principal component analyzers
Neural Computation
Construction of vector field hierarchies
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Simplified representation of vector fields
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
A Phase Field Model for Continuous Clustering on Vector Fields
IEEE Transactions on Visualization and Computer Graphics
Segmentation of Discrete Vector Fields
IEEE Transactions on Visualization and Computer Graphics
Similarity measure for vector field learning
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Supervised learning for classification
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Similarity measure for vector field learning
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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In this paper, vector field learning is proposed as a new application of manifold learning to vector field. We also provide a learning framework to extract significant features from vector data. Vector data containing position, direction and magnitude information is different from common point data only containing position information. The algorithm of locally linear embedding (LLE) is extended to deal with vector data. The learning ability of the extended version has been tested on synthetic data sets and experimental results demonstrate that the method is very helpful and promising. Manifold features of vector data obtained by learning methods can be used for next work such as classification, clustering, visualization, or segmentation of vectors.