Recent Submissions

  • A Theoretical Analysis of Joint Manifolds 

    Davenport, Mark A.; Hegde, Chinmay; Duarte, Marco; Baraniuk, Richard G. (2009-01)
    The emergence of low-cost sensor architectures for diverse modalities has made it possible to deploy sensor arrays that capture a single event from a large number of vantage points and using multiple modalities. In many ...
  • Sparse Coding with Population Sketches 

    Dyer, Eva L.; Baraniuk, Richard G.; Johnson, Don H. (2009-07-13)
  • Fast, Exact Synthesis of Gaussian and nonGaussian Long-Range-Dependent Processes 

    Baraniuk, Richard; Crouse, Matthew (2009-04-15)
    1/f noise and statistically self-similar random processes such as fractional Brownian motion (fBm) and fractional Gaussian noise (fGn) are fundamental models for a host of real-world phenomena, from network traffic to ...
  • Tuning support vector machines for minimax and Neyman-Pearson classification 

    Scott, Clayton D.; Baraniuk, Richard G.; Davenport, Mark A. (2008-08-19)
    This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and Neyman-Pearson criteria. In principle, these criteria can be optimized in a straightforward way using a cost-sensitive ...
  • A simple proof of the restricted isometry property for random matrices 

    Baraniuk, Richard G.; Davenport, Mark A.; DeVore, Ronald A.; Wakin, Michael B. (2007-01-18)
    We give a simple technique for verifying the Restricted Isometry Property (as introduced by Candès and Tao) for random matrices that underlies Compressed Sensing. Our approach has two main ingredients: (i) concentration ...
  • Single-pixel imaging via compressive sampling 

    Duarte, Marco F.; Davenport, Mark A.; Takhar, Dharmpal; Laska, Jason N.; Sun, Ting; Kelly, Kevin F.; Baraniuk, Richard G. (2008-03-01)
  • Multiscale random projections for compressive classification 

    Duarte, Marco F.; Davenport, Mark A.; Wakin, Michael B.; Laska, Jason N.; Takhar, Dharmpal; Kelly, Kevin F.; Baraniuk, Richard G. (2007-09-01)
    We propose a framework for exploiting dimension-reducing random projections in detection and classification problems. Our approach is based on the generalized likelihood ratio test; in the case of image classification, ...
  • Minimax support vector machines 

    Davenport, Mark A.; Baraniuk, Richard G.; Scott, Clayton D. (2007-08-01)
    We study the problem of designing support vector machine (SVM) classifiers that minimize the maximum of the false alarm and miss rates. This is a natural classification setting in the absence of prior information regarding ...
  • The smashed filter for compressive classification and target recognition 

    Davenport, Mark A.; Duarte, Marco F.; Wakin, Michael B.; Laska, Jason N.; Takhar, Dharmpal; Kelly, Kevin F.; Baraniuk, Richard G. (2007-01-01)
    The theory of compressive sensing (CS) enables the reconstruction of a sparse or compressible image or signal from a small set of linear, non-adaptive (even random) projections. However, in many applications, including ...
  • Regression level set estimation via cost-sensitive classification 

    Scott, Clayton D.; Davenport, Mark A. (2007-06-01)
    Regression level set estimation is an important yet understudied learning task. It lies somewhere between regression function estimation and traditional binary classification, and in many cases is a more appropriate setting ...
  • Detection and estimation with compressive measurements 

    Baraniuk, Richard G.; Davenport, Mark A.; Wakin, Michael B. (2006-11-01)
    The recently introduced theory of compressed sensing enables the reconstruction of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can ...
  • Small-time scaling behaviors of Internet backbone traffic: An empirical study 

    Zhang, Zhi-Li; Ribeiro, Vinay Joseph; Moon, Sue; Diot, Christophe (2003-04-20)
    We study the small-time (sub-seconds) scaling behaviors of Internet backbone traffic, based on traces collected from OC3/12/48 links in a tier-1 ISP. We observe that for a majority of these traces, the (second-order) scaling ...
  • Multiscale Density Estimation 

    Willett, Rebecca; Nowak, Robert David (2003-08-20)
    The nonparametric density estimation method proposed in this paper is computationally fast, capable of detecting density discontinuities and singularities at a very high resolution, spatially adaptive, and offers near ...
  • Multiscale Likelihood Analysis and Image Reconstruction 

    Willett, Rebecca; Nowak, Robert David (2003-08-20)
    The nonparametric multiscale polynomial and platelet methods presented here are powerful new tools for signal and image denoising and reconstruction. Unlike traditional wavelet-based multiscale methods, these methods are ...
  • Platelets for Multiscale Analysis in Photon-Limited Imaging 

    Willett, Rebecca; Nowak, Robert David (2002-09-20)
    This paper proposes a new multiscale image decomposition based on platelets. Platelets are localized functions at various scales, locations, and orientations that produce piecewise linear image approximations. For smoothness ...
  • Platelets for Multiscale Analysis in Medical Imaging 

    Willett, Rebecca; Nowak, Robert David (2002-04-20)
    This paper describes the development and use of multiscale, platelet-based image reconstruction algorithms in medical imaging. Such algorithms are effective because platelets approximate images in certain (piecewise) ...
  • Multiscale Analysis for Intensity and Density Estimation 

    Willett, Rebecca (2002-04-20)
    The nonparametric multiscale polynomial and platelet algorithms presented in this thesis are powerful new tools for signal and image denoising and reconstruction. Unlike traditional wavelet-based multiscale methods, these ...
  • Platelets: A Multiscale Approach for Recovering Edges and Surfaces in Photon-Limited Medical Imaging 

    Willett, Rebecca; Nowak, Robert David (2001-10-20)
    This paper proposes a new multiscale image decomposition based on platelets. Platelets are localized functions at various scales, locations, and orientations that produce piecewise linear image approximations. Platelets ...
  • Multiresolution Nonparametric Intensity and Density Estimation 

    Willett, Rebecca; Nowak, Robert David (2002-05-20)
    This paper introduces a new multiscale method for nonparametric piecewise polynomial intensity and density estimation of point processes. Fast, piecewise polynomial, maximum penalized likelihood methods for intensity and ...
  • Multiresolution Intensity Estimation of Piecewise Linear Poisson Processes 

    Willett, Rebecca (2001-04-20)
    Given observations of a one-dimensional piecewise linear, length-M Poisson intensity function, our goal is to estimate both the partition points and the parameters of each segment. In order to determine where the breaks ...

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