Algorithms for manifold learning

@inproceedings{Cayton2005AlgorithmsFM,
  title={Algorithms for manifold learning},
  author={Lawrence Cayton},
  year={2005}
}
Manifold learning is a popular recent approach to nonlinear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high; though each data point consists of perhaps thousands of features, it may be described as a function of only a few underlying parameters. That is, the data points are actually samples from a low-dimensional manifold that is embedded in a high-dimensional space. Manifold learning algorithms attempt… CONTINUE READING
Highly Cited
This paper has 199 citations. REVIEW CITATIONS
108 Citations
27 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 108 extracted citations

199 Citations

0102030'09'12'15'18
Citations per Year
Semantic Scholar estimates that this publication has 199 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 27 references

SIAM Journal of Scientific Computing

  • Zhenyue Zhang, Hongyuan Zha. Principal manifolds, nonlinear dimension reduction via local tangent space alignment
  • 26(1):313–338,
  • 2004
Highly Influential
4 Excerpts

15(6):1373–1396

  • Mikhail Belkin, Partha Niyogi. Laplacian eigenmaps for dimensionality reduction, data representation. Neural Computation
  • June
  • 2003
Highly Influential
4 Excerpts

Multidimensional Scaling

  • T. F. Cox, M.A.A. Cox
  • Chapman and Hall/CRC, 2nd edition
  • 2001
Highly Influential
3 Excerpts

Burges . Geometric methods for feature extraction and dimensional reduction

  • A. M.A.
  • 2005

Cost functions for euclidean embedding

  • Lawrence Cayton, Sanjoy Dasgupta
  • Unpublished manuscript,
  • 2005
2 Excerpts

Similar Papers

Loading similar papers…