Multiscale Geometric Methods for Data Sets II: Geometric Multi-Resolution Analysis
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[1] M. Maggioni,et al. Determination of reaction coordinates via locally scaled diffusion map. , 2011, The Journal of chemical physics.
[2] Wolfgang Dahmen,et al. Fast high-dimensional approximation with sparse occupancy trees , 2011, J. Comput. Appl. Math..
[3] Robert H. Halstead,et al. Matrix Computations , 2011, Encyclopedia of Parallel Computing.
[4] Guangliang Chen,et al. Data representation and exploration with Geometric Wavelets , 2010, 2010 IEEE Symposium on Visual Analytics Science and Technology.
[5] Guillermo Sapiro,et al. Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..
[6] Guillermo Sapiro,et al. Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations , 2009, NIPS.
[7] Arthur Szlam,et al. Asymptotic regularity of subdivisions of Euclidean domains by iterated PCA and iterated 2-means , 2009 .
[8] Mauro Maggioni,et al. Multiscale Estimation of Intrinsic Dimensionality of Data Sets , 2009, AAAI Fall Symposium: Manifold Learning and Its Applications.
[9] M. Maggioni,et al. Estimation of intrinsic dimensionality of samples from noisy low-dimensional manifolds in high dimensions with multiscale SVD , 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing.
[10] Guillermo Sapiro,et al. Discriminative k-metrics , 2009, ICML '09.
[11] Michael L. Littman,et al. Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009 , 2009, International Conference on Machine Learning.
[12] Guillermo Sapiro,et al. Online dictionary learning for sparse coding , 2009, ICML '09.
[13] Richard G. Baraniuk,et al. Random Projections of Smooth Manifolds , 2009, Found. Comput. Math..
[14] Mark Tygert,et al. A Randomized Algorithm for Principal Component Analysis , 2008, SIAM J. Matrix Anal. Appl..
[15] Ronald R. Coifman,et al. Regularization on Graphs with Function-adapted Diffusion Processes , 2008, J. Mach. Learn. Res..
[16] Stephen Smale,et al. Finding the Homology of Submanifolds with High Confidence from Random Samples , 2008, Discret. Comput. Geom..
[17] M. Maggioni,et al. Manifold parametrizations by eigenfunctions of the Laplacian and heat kernels , 2008, Proceedings of the National Academy of Sciences.
[18] Sridhar Mahadevan,et al. Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes , 2007, J. Mach. Learn. Res..
[19] M. Maggioni,et al. Universal Local Parametrizations via Heat Kernels and Eigenfunctions of the Laplacian , 2007, 0709.1975.
[20] Robert M. Haralick,et al. Nonlinear Manifold Clustering By Dimensionality , 2006, 18th International Conference on Pattern Recognition (ICPR'06).
[21] Arthur D. Szlam,et al. Diffusion wavelet packets , 2006 .
[22] John Langford,et al. Cover trees for nearest neighbor , 2006, ICML.
[23] Sridhar Mahadevan,et al. Fast direct policy evaluation using multiscale analysis of Markov diffusion processes , 2006, ICML.
[24] Ronald R. Coifman,et al. Geometries of sensor outputs, inference, and information processing , 2006, SPIE Defense + Commercial Sensing.
[25] Ronald R. Coifman,et al. Qeeg-Based Classification With Wavelet Packet and Microstate Features for Triage Applications in the ER , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[26] Stéphane Lafon,et al. Diffusion maps , 2006 .
[27] Wolfgang Dahmen,et al. Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions , 2005, J. Mach. Learn. Res..
[28] Michael Elad,et al. Submitted to Ieee Transactions on Image Processing Image Decomposition via the Combination of Sparse Representations and a Variational Approach , 2022 .
[29] Ronald R. Coifman,et al. Biorthogonal diffusion wavelets for multiscale representations on manifolds and graphs , 2005, SPIE Optics + Photonics.
[30] Ronald R. Coifman,et al. Diffusion-driven multiscale analysis on manifolds and graphs: top-down and bottom-up constructions , 2005, SPIE Optics + Photonics.
[31] Ann B. Lee,et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[32] R R Coifman,et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: multiscale methods. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[33] Michael Elad,et al. K-SVD : DESIGN OF DICTIONARIES FOR SPARSE REPRESENTATION , 2005 .
[34] Peter Schröder,et al. Multiscale Representations for Manifold-Valued Data , 2005, Multiscale Model. Simul..
[35] H. Zha,et al. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment , 2004, SIAM J. Sci. Comput..
[36] Alfred O. Hero,et al. Learning intrinsic dimension and intrinsic entropy of high-dimensional datasets , 2004, 2004 12th European Signal Processing Conference.
[37] Ronald A. DeVore,et al. Fast computation in adaptive tree approximation , 2004, Numerische Mathematik.
[38] Francesco Camastra,et al. Intrinsic Dimension Estimation of Data: An Approach Based on Grassberger–Procaccia's Algorithm , 2001, Neural Processing Letters.
[39] David R. Larson,et al. Wavelets, frames and operator theory : Focused Research Group Workshop on Wavelets, Frames and Operator Theory, January 15-21, 2003, University of Maryland, College Park, Maryland , 2004 .
[40] R. Coifman,et al. Diffusion Wavelets , 2004 .
[41] P. Casazza,et al. Frames of subspaces , 2003, math/0311384.
[42] D. Donoho,et al. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[43] Hongyuan Zha,et al. Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment , 2022 .
[44] D. Donoho,et al. Hessian Eigenmaps : new locally linear embedding techniques for high-dimensional data , 2003 .
[45] O. Christensen. An introduction to frames and Riesz bases , 2002 .
[46] A. Vinciarelli,et al. Estimating the Intrinsic Dimension of Data with a Fractal-Based Method , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[47] Mikhail Belkin,et al. Using manifold structure for partially labelled classification , 2002, NIPS 2002.
[48] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[49] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[50] S. Semmes,et al. Uniform rectifiability and quasiminimizing sets of arbitrary codimension , 2000 .
[51] E. Candès,et al. Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges , 2000 .
[52] Vipin Kumar,et al. A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs , 1998, SIAM J. Sci. Comput..
[53] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[54] S. Mallat. A wavelet tour of signal processing , 1998 .
[55] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[56] Yair Bartal,et al. Probabilistic approximation of metric spaces and its algorithmic applications , 1996, Proceedings of 37th Conference on Foundations of Computer Science.
[57] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[58] D. Donoho,et al. Translation-Invariant De-Noising , 1995 .
[59] Ronald R. Coifman,et al. Signal processing and compression with wavelet packets , 1994 .
[60] Yves Meyer,et al. Progress in wavelet analysis and applications , 1993 .
[61] S. Semmes,et al. Analysis of and on uniformly rectifiable sets , 1993 .
[62] Ingrid Daubechies,et al. Ten Lectures on Wavelets , 1992 .
[63] G. David. Wavelets and Singular Integrals on Curves and Surfaces , 1991 .
[64] The Traveling Salesman problem and Harmonic analysis , 1991 .
[65] Peter W. Jones. Rectifiable sets and the Traveling Salesman Problem , 1990 .
[66] Michael Christ,et al. A T(b) theorem with remarks on analytic capacity and the Cauchy integral , 1990 .
[67] S. Mallat. Multiresolution approximations and wavelet orthonormal bases of L^2(R) , 1989 .
[68] Stéphane Mallat,et al. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..