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Local Multidimensional Scaling for Nonlinear Dimension Reduction, Graph Drawing, and Proximity Analysis

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  • Chen, Lisha
  • Buja, Andreas

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  • Chen, Lisha & Buja, Andreas, 2009. "Local Multidimensional Scaling for Nonlinear Dimension Reduction, Graph Drawing, and Proximity Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 209-219.
    • Handle: RePEc:bes:jnlasa:v:104:i:485:y:2009:p:209-219
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    Cited by:

    1. Luwan Zhang & Grace Wahba & Ming Yuan, 2016. "Distance shrinkage and Euclidean embedding via regularized kernel estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 849-867, September.
    2. Harold A. Hernández-Roig & M. Carmen Aguilera-Morillo & Rosa E. Lillo, 2021. "Functional Modeling of High-Dimensional Data: A Manifold Learning Approach," Mathematics, MDPI, vol. 9(4), pages 1-22, February.
    3. Fan Cheng & Rob J Hyndman & Anastasios Panagiotelis, 2021. "Manifold Learning with Approximate Nearest Neighbors," Monash Econometrics and Business Statistics Working Papers 3/21, Monash University, Department of Econometrics and Business Statistics.
    4. Silva, F.N. & Rodrigues, F.A. & Oliveira, O.N. & da F. Costa, L., 2013. "Quantifying the interdisciplinarity of scientific journals and fields," Journal of Informetrics, Elsevier, vol. 7(2), pages 469-477.
    5. Gruenhage, Gina & Opper, Manfred & Barthelme, Simon, 2016. "Visualizing the effects of a changing distance on data using continuous embeddings," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 51-65.
    6. Jonas Paulsen & Odin Gramstad & Philippe Collas, 2015. "Manifold Based Optimization for Single-Cell 3D Genome Reconstruction," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-19, August.

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