Learning Nonlinear Manifolds from Time Series

  title={Learning Nonlinear Manifolds from Time Series},
  author={Ruei-Sung Lin and Che-Bin Liu and Ming-Hsuan Yang and Narendra Ahuja and Stephen E. Levinson},
There has been growing interest in developing nonlinear dim ensionality reduction algorithms for vision applications. Altho ugh progress has been made in recent years, conventional nonlinear dimensionali ty reduction algorithms have been designed to deal with stationary, or independent a nd identically distributed data. In this paper, we present a novel method that learn s nonlinear mapping from time series data to their intrinsic coordinates on the u nderlying manifold. Our work extends the… CONTINUE READING
Highly Cited
This paper has 50 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 33 extracted citations

51 Citations

Citations per Year
Semantic Scholar estimates that this publication has 51 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 15 references

A global ge om tric framework for nonlinear dimensionality reduction

  • J. B. Tenenbaum, V. de Silva, J. C. Langford
  • Science 290
  • 2000
Highly Influential
6 Excerpts

Similar Papers

Loading similar papers…