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Guoshen Yu

  1. Task-Driven Adaptive Statistical Compressive Sensing of Gaussian Mixture Models.

    Fri, 01/27/2012 - 15:01 — SomNambul
    Authors: Guillermo Sapiro, Guoshen Yu, Lawrence Carin, Julio M. Duarte-Carvajalino
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    A framework for adaptive and non-adaptive statistical compressive sensing is
    developed, where a statistical model replaces the standard sparsity model of
    classical compressive sensing. We propose within this framework optimal
    task-specific sensing protocols specifically and jointly designed for
    classification and reconstruction. A two-step adaptive sensing paradigm is
    developed, where online sensing is applied to detect the signal class in the
    first step, followed by a reconstruction step adapted to the detected class and
    the observed samples.

  2. Statistical Compressed Sensing of Gaussian Mixture Models.

    Tue, 02/01/2011 - 16:01 — SomNambul
    Authors: Guillermo Sapiro, Guoshen Yu
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    A novel framework of compressed sensing, namely statistical compressed
    sensing (SCS), that aims at efficiently sampling a collection of signals that
    follow a statistical distribution, and achieving accurate reconstruction on
    average, is introduced.

  3. Statistical Compressive Sensing of Gaussian Mixture Models.

    Fri, 10/22/2010 - 15:01 — SomNambul
    Authors: Guillermo Sapiro, Guoshen Yu
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    A new framework of compressive sensing (CS), namely statistical compressive
    sensing (SCS), that aims at efficiently sampling a collection of signals that
    follow a statistical distribution and achieving accurate reconstruction on
    average, is introduced.

  4. Efficient Matrix Completion with Gaussian Models.

    Thu, 10/21/2010 - 15:01 — SomNambul
    Authors: Guillermo Sapiro, Guoshen Yu, Flavien Léger
    Subjects: Learning
    Abstract

    A general framework based on Gaussian models and a MAP-EM algorithm is
    introduced in this paper for solving matrix/table completion problems. The
    numerical experiments with the standard and challenging movie ratings data show
    that the proposed approach, based on probably one of the simplest probabilistic
    models, leads to the results in the same ballpark as the state-of-the-art, at a
    lower computational cost.

  5. Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity.

    Wed, 06/16/2010 - 15:01 — SomNambul
    Authors: Stéphane Mallat, Guillermo Sapiro, Guoshen Yu
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    A general framework for solving image inverse problems is introduced in this
    paper. The approach is based on Gaussian mixture models, estimated via a
    computationally efficient MAP-EM algorithm. A dual mathematical interpretation
    of the proposed framework with structured sparse estimation is described, which
    shows that the resulting piecewise linear estimate stabilizes the estimation
    when compared to traditional sparse inverse problem techniques. This
    interpretation also suggests an effective dictionary motivated initialization
    for the MAP-EM algorithm.

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