A multi-scale approach for data imputation
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[1] Ronald R. Coifman,et al. Heterogeneous Datasets Representation and Learning using Diffusion Maps and Laplacian Pyramids , 2012, SDM.
[2] José R. Dorronsoro,et al. Auto-adaptative Laplacian Pyramids for high-dimensional data analysis , 2013, ArXiv.
[3] Stéphane Lafon,et al. Diffusion maps , 2006 .
[4] José R. Dorronsoro,et al. Auto-adaptive Laplacian Pyramids , 2016, ESANN.
[5] Muhammad Tayyab Asif,et al. Low-dimensional models for missing data imputation in road networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[6] D. Rubin,et al. Statistical Analysis with Missing Data , 1988 .
[7] D. Rubin. Multiple Imputation After 18+ Years , 1996 .
[8] Yelipe UshaRani,et al. An efficient disease prediction and classification using feature reduction based imputation technique , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).
[9] Neta Rabin,et al. Missing Data Completion Using Diffusion Maps and Laplacian Pyramids , 2017, ICCSA.
[10] Mark Huisman,et al. Missing data in behavioral science research: Investigation of a collection of data sets , 1998 .
[11] Kevin R. Moon,et al. MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data , 2017, bioRxiv.
[12] Emma Pierson,et al. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis , 2015, Genome Biology.
[13] Ronen Talmon,et al. Nonlinear intrinsic variables and state reconstruction in multiscale simulations. , 2013, The Journal of chemical physics.