Summarizing Large Data Sets
Jeffrey A. Bilmes

Citation
Jeffrey A. Bilmes. "Summarizing Large Data Sets". Tutorial, 20, August, 2015.

Abstract
The recent growth of available data is both a blessing and a curse for the field of data science. While large data sets can lead to improved predictive accuracy and can motivate research in parallel computing, they can also be plagued with redundancy, leading to wasted computation. In this talk we will discuss a class of approaches to data summarization and subset selection based on submodular functions. We will see how a form of "combinatorial dependence" over data sets can be naturally induced via submodular functions, and how resulting submodular programs (that often have approximation guarantees) can yield practical and high-quality data summarization strategies. The effectiveness of this approach will be demonstrated based on results from a wide range of applications, including document summarization, machine learning training data subset selection, image summarization, and some recent work on online streaming video summarization.

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Citation formats

• HTML

  Jeffrey A. Bilmes. <a
  href="http://www.terraswarm.org/pubs/595.html"
  ><i>Summarizing Large Data
  Sets</i></a>, Tutorial,  20, August, 2015.

• Plain text

  Jeffrey A. Bilmes. "Summarizing Large Data Sets".
  Tutorial,  20, August, 2015.

• BibTeX

  @tutorial{Bilmes15_SummarizingLargeDataSets,
      author = {Jeffrey A. Bilmes},
      title = {Summarizing Large Data Sets},
      day = {20},
      month = {August},
      year = {2015},
      abstract = {The recent growth of available data is both a
                blessing and a curse for the field of data
                science. While large data sets can lead to
                improved predictive accuracy and can motivate
                research in parallel computing, they can also be
                plagued with redundancy, leading to wasted
                computation. In this talk we will discuss a class
                of approaches to data summarization and subset
                selection based on submodular functions. We will
                see how a form of "combinatorial dependence" over
                data sets can be naturally induced via submodular
                functions, and how resulting submodular programs
                (that often have approximation guarantees) can
                yield practical and high-quality data
                summarization strategies. The effectiveness of
                this approach will be demonstrated based on
                results from a wide range of applications,
                including document summarization, machine learning
                training data subset selection, image
                summarization, and some recent work on online
                streaming video summarization. },
      URL = {http://terraswarm.org/pubs/595.html}
  }
  

Posted by Barb Hoversten on 30 Jul 2015.
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