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Searching Results for SW10.27-30.08:
The best low-rank Tucker approximation of a tensor, Lars Eldén  (Linköping University)
A Geometric perspective on machine Learning, Partha Niyogi  (University of Chicago)
Detecting mixed dimensionality and density in noisy point clouds, Gloria Haro Ortega  (Universitat Politecnica de Catalunya)
Harmonic and multiscale analysis on low-dimensional data sets in high-dimensions, Mauro Maggioni  (Duke University)
Manifold models for signal acquisition, compression, and processing, Richard G. Baraniuk  (Rice University)
Large group discussion on What have we learned about manifold learning — what are its implications for machine learning and numerical analysis? What are open questions? What are successes? Where should we be optimistic and where should we be pessimistic?, Partha Niyogi  (University of Chicago)
Multilinear (tensor) manifold data modeling, M. Alex O. Vasilescu  (SUNY)
Recovering sparsity in high dimensions, Ronald DeVore  (Texas A & M University)
Clustering linear and nonlinear manifolds, René Vidal  (Johns Hopkins University)
Instance optimal adaptive regression in high dimensions, Wolfgang Dahmen  (RWTH Aachen)
Spectral and geometric methods in learning, Mikhail Belkin  (Ohio State University)
Large group discussion on:
1. The representation of high-level information and low-level data
2. The symbiotic linkage between information and data
3. The need to transform qualitative information into quantitative data sets and vice versa
4. The need to think beyond the learning for classification.
5. How mathematics can be useful to the aforementioned domains of interest in conjunction with information integration and data fusion., Tristan Nguyen  (Office of Naval Research)
Interpolation of functions on Rn, Charles L. Fefferman  (Princeton University)
Multi-manifold data modeling via spectral curvature clustering, Gilad Lerman  (University of Minnesota)
Visualization & matching for graphs and data, Tony Jebara  (Columbia University)
Topology and data, Gunnar Carlsson  (Stanford University)
Dense error correction via L1 minimization, Yi Ma  (University of Illinois at Urbana-Champaign)
Large group discussion on Manifold Clustering
1) What have have been recent advances on manifold clustering?
a) Algebraic approaches
b) Spectral approaches
c) Probabilistic approaches

2) What have been successful applications of manifold clustering?
3) What is the role of topology, geometry, and statistics, in manifold learning, i.e.,
a) clustering based on the dimensions of the manifolds
b) clustering based on geometry
c) clustering based on statistics

3) What are the open problems in manifold clustering?, René Vidal  (Johns Hopkins University)
CPOPT: Optimization for fitting CANDECOMP/PARAFAC models, Tamara G. Kolda  (Sandia National Laboratories)
Mathematical problems suggested by Analog-to-Digital conversion, Ingrid Daubechies  (Princeton University)
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