Abstract
Keeping items in order is at the essence of organizing information. This paper derives an information-theoretic method for approximate sorting. It is optimal in the sense that it extracts as much reliable order information as possible from possibly noisy comparison input data.
The information-theoretic method for approximate sorting is based on approximation sets for a sorting cost function. It optimizes the tradeoff between localizing a set of solutions in a solution space and “robustifying” solution sets against noise in the comparisons. The method is founded on the maximum approximation capacity principle [3,4]. The validity of the new method and its superior rank prediction capability are demonstrated by sorting experiments on real world data.
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Busse, L., Haghir Chehreghani, M., Buhmann, J.M. (2013). Approximate Sorting. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_15
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DOI: https://doi.org/10.1007/978-3-642-40602-7_15
Publisher Name: Springer, Berlin, Heidelberg
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