Citation
Ehsan Elhamifar, Guillermo Sapiro, Allen Yang, Shankar S. Sastry. "A Convex Optimization Framework for Active Learning". International Conference on Computer Vision (ICCV), 2013.

Abstract
In many image/video/web classification problems, we have access to a large number of unlabeled samples. However, it is typically expensive and time consuming to obtain labels for the samples. Active learning is the problem of progressively selecting and annotating the most informative unlabeled samples, in order to obtain a high classification performance. Most existing active learning algorithms select only one sample at a time prior to retraining the classifier. Hence, they are computationally expensive and cannot take advantage of parallel labeling systems such as Mechanical Turk. On the other hand, algorithms that allow the selection of multiple samples prior to retraining the classifier, may select samples that have significant information overlap or they involve solving a non-convex optimization. More importantly, the majority of active learning algorithms are developed for a certain classifier type such as SVM. In this paper, we develop an efficient active learning framework based on convex programming, which can select multiple samples at a time for annotation. Unlike the state of the art, our algorithm can be used in conjunction with any type of classifiers, including those of the family of the recently proposed Sparse Representation-based Classification (SRC). We use the two principles of classifier uncertainty and sample diversity in order to guide the optimization program towards selecting the most informative unlabeled samples, which have the least information overlap. Our method can incorporate the data distribution in the selection process by using the appropriate dissimilarity between pairs of samples. We show the effectiveness of our framework in person detection, scene categorization and face recognition on real-world datasets.

Electronic downloads

• http://www.cv-foundation.org/openaccess/content_iccv_2013/html/Elhamifar_A_Convex_Optimization_2013_ICCV_paper.html

• HTML

  Ehsan Elhamifar, Guillermo Sapiro, Allen Yang, Shankar S.
  Sastry. <a
  href="http://robotics.eecs.berkeley.edu/pubs/2.html"
  >A Convex Optimization Framework for Active
  Learning</a>, International Conference on Computer
  Vision (ICCV), 2013.

• Plain text

  Ehsan Elhamifar, Guillermo Sapiro, Allen Yang, Shankar S.
  Sastry. "A Convex Optimization Framework for Active
  Learning". International Conference on Computer Vision
  (ICCV), 2013.

• BibTeX

  @inproceedings{ElhamifarSapiroYangSastry13_ConvexOptimizationFrameworkForActiveLearning,
      author = {Ehsan Elhamifar and Guillermo Sapiro and Allen
                Yang and Shankar S. Sastry},
      title = {A Convex Optimization Framework for Active Learning},
      booktitle = {International Conference on Computer Vision (ICCV)},
      year = {2013},
      abstract = {In many image/video/web classification problems,
                we have access to a large number of unlabeled
                samples. However, it is typically expensive and
                time consuming to obtain labels for the samples.
                Active learning is the problem of progressively
                selecting and annotating the most informative
                unlabeled samples, in order to obtain a high
                classification performance. Most existing active
                learning algorithms select only one sample at a
                time prior to retraining the classifier. Hence,
                they are computationally expensive and cannot take
                advantage of parallel labeling systems such as
                Mechanical Turk. On the other hand, algorithms
                that allow the selection of multiple samples prior
                to retraining the classifier, may select samples
                that have significant information overlap or they
                involve solving a non-convex optimization. More
                importantly, the majority of active learning
                algorithms are developed for a certain classifier
                type such as SVM. In this paper, we develop an
                efficient active learning framework based on
                convex programming, which can select multiple
                samples at a time for annotation. Unlike the state
                of the art, our algorithm can be used in
                conjunction with any type of classifiers,
                including those of the family of the recently
                proposed Sparse Representation-based
                Classification (SRC). We use the two principles of
                classifier uncertainty and sample diversity in
                order to guide the optimization program towards
                selecting the most informative unlabeled samples,
                which have the least information overlap. Our
                method can incorporate the data distribution in
                the selection process by using the appropriate
                dissimilarity between pairs of samples. We show
                the effectiveness of our framework in person
                detection, scene categorization and face
                recognition on real-world datasets.},
      URL = {http://robotics.eecs.berkeley.edu/pubs/2.html}
  }
  

Posted by Ehsan Elhamifar on 7 May 2014.
Groups: ehumans
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