A global geometric framework for nonlinear dimensionality reduction.

@article{Tenenbaum2000AGG,
  title={A global geometric framework for nonlinear dimensionality reduction.},
  author={Joshua B. Tenenbaum and Vin De Silva and John C. Langford},
  journal={Science},
  year={2000},
  volume={290 5500},
  pages={
          2319-23
        }
}
Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers or 10(6) optic nerve fibers-a manageably small number… CONTINUE READING

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