Approximation Algorithms for Classification Problems with Pairwise Relationships: Metric Labeling and Markov Random Fields

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Approximation Algorithms for Classification Problems with Pairwise Relationships: Metric Labeling and Markov Random Fields (1999)

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by Jon Kleinberg , Éva Tardos

Venue:	IN IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE
Citations:	197 - 2 self

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@INPROCEEDINGS{Kleinberg99approximationalgorithms,
    author = {Jon Kleinberg and Éva Tardos},
    title = {Approximation Algorithms for Classification Problems with Pairwise Relationships: Metric Labeling and Markov Random Fields},
    booktitle = {IN IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE},
    year = {1999},
    pages = {14--23},
    publisher = {}
}

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Abstract

In a traditional classification problem, we wish to assign one of k labels (or classes) to each of n objects, in a way that is consistent with some observed data that we have about the problem. An active line of research in this area is concerned with classification when one has information about pairwise relationships among the objects to be classified; this issue is one of the principal motivations for the framework of Markov random fields, and it arises in areas such as image processing, biometry, and document analysis. In its most basic form, this style of analysis seeks a classification that optimizes a combinatorial function consisting of assignment costs --- based on the individual choice of label we make for each object --- and separation costs --- based on the pair of choices we make for two "related" objects. We formulate a general classification problem of this type, the metric labeling problem; we show that it contains as special cases a number of standard classification f...

Keyphrases

markov random field pairwise relationship classification problem metric labeling approximation algorithm standard classification general classification problem related object active line assignment cost individual choice metric labeling problem traditional classification problem combinatorial function consisting separation cost basic form special case image processing principal motivation document analysis

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