Adaptive Manifold Learning

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Jing Wang, Zhenyue Zhang, Hongyuan Zha


Recently, there have been several advances in the machine learning and pattern recognition communities for developing manifold learning algo- rithms to construct nonlinear low-dimensional manifolds from sample data points embedded in high-dimensional spaces. In this paper, we de- velop algorithms that address two key issues in manifold learning: 1) the adaptive selection of the neighborhood sizes; and 2) better fitting the local geometric structure to account for the variations in the curvature of the manifold and its interplay with the sampling density of the data set. We also illustrate the effectiveness of our methods on some synthetic data sets.