Learning Gradients and Feature Selection on Manifolds

@inproceedings{MukherjeeLearningGA,
  title={Learning Gradients and Feature Selection on Manifolds},
  author={Sayan Mukherjee and Qiang Wu and Ding-Xuan Zhou}
}
An underlying premise in the analysis and modeling of high-dimensional physical and biological systems is that data generated by measuring thousands of variables lies on or near a low-dimensional manifold. This premise has led to various estimation and learning problems grouped under the heading of “manifold learning.” It is natural to formulate the problem of feature selection – finding salient variables (or linear combinations of salient variables) and estimating how they covary – in the… CONTINUE READING

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