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Narendra Ahuja

  1. A Probabilistic Framework for Discriminative Dictionary Learning.

    Втр, 09/13/2011 - 15:01 — SomNambul
    Authors: Bernard Ghanem, Narendra Ahuja
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    In this paper, we address the problem of discriminative dictionary learning
    (DDL), where sparse linear representation and classification are combined in a
    probabilistic framework. As such, a single discriminative dictionary and linear
    binary classifiers are learned jointly. By encoding sparse representation and
    discriminative classification models in a MAP setting, we propose a general
    optimization framework that allows for a data-driven tradeoff between faithful
    representation and accurate classification.

  2. MIS-Boost: Multiple Instance Selection Boosting.

    Втр, 09/13/2011 - 15:01 — SomNambul
    Authors: Emre Akbas, Bernard Ghanem, Narendra Ahuja
    Subjects: Learning
    Abstract

    In this paper, we present a new multiple instance learning (MIL) method,
    called MIS-Boost, which learns discriminative instance prototypes by explicit
    instance selection in a boosting framework. Unlike previous instance selection
    based MIL methods, we do not restrict the prototypes to a discrete set of
    training instances but allow them to take arbitrary values in the instance
    feature space. We also do not restrict the total number of prototypes and the
    number of selected-instances per bag; these quantities are completely
    data-driven.

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