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Statistical Analysis of Metric Graph Reconstruction
Metric Graph Filament Reconstruction Manifold Learning Minimax Esti-mation
2013/6/13
A metric graph is a 1-dimensional stratified metric space consisting of vertices and edges or loops glued together. Metric graphs can be naturally used to represent and model data that take the form o...
Bandlimited Signal Reconstruction From the Distribution of Unknown Sampling Locations
Bandlimited Signal Reconstruction the Distribution Unknown Sampling Locations
2013/4/28
We study the reconstruction of bandlimited fields from samples taken at unknown but statistically distributed sampling locations. The setup is motivated by distributed sampling where precise knowledge...
Structure-Based Bayesian Sparse Reconstruction
Structure-Based Bayesian Sparse Reconstruction
2012/9/19
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the spars...
A perturbative approach to the reconstruction of the eigenvalue spectrum of a normal covariance matrix from a spherically truncated counterpart
A perturbative approach the reconstruction the eigenvalue spectrum a normal covariance matrix a spherically truncated counterpart
2012/9/18
In this paper we propose a perturbative method for the reconstruction of the covariance matrix of a multinormal distribution, under the assumption that the only available information amounts to the co...
Reconstruction from anisotropic random measurements
Reconstruction anisotropic random measurements
2011/7/5
Random matrices are widely used in sparse recovery problems, and the relevant properties of matrices with i.i.d. entries are well understood.
Reconstruction of Fractional Brownian Motion Signals From Its Sparse Samples Based on Compressive Sampling
Compressive Sampling fractional Brownian motion interpolation financial time-series fractal
2011/6/21
This paper proposes a new fBm (fractional Brownian
motion) interpolation/reconstruction method from partially
known samples based on CS (Compressive Sampling). Since 1/f
property implies power law ...
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction
Low-Rank Matrix Reconstruction
2011/3/25
We consider the problem of approximately reconstructing a partially-observed, approximately low-rank matrix. This problem has received much attention lately, mostly using the trace-norm as a surrogate...
Reconstruction of signals with unknown spectra in information field theory with parameter uncertainty
Reconstruction signals unknown spectra information field theory parameter uncertainty
2010/3/11
The optimal reconstruction of cosmic metric perturbations and other signals requires knowledge
of their power spectra and other parameters. If these are not known a priori, they have to be
measured ...