Abstract
Ordered lists of objects are widely used as representational forms. Such ordered objects include Web search results and bestseller lists. Techniques for processing such ordinal data are being developed, particularly methods for an object ranking task: i.e., learning functions used to sort objects from sample orders. In this article, we propose two dimension reduction methods specifically designed to improve prediction performance in an object ranking task. © 2011 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Kamishima, T., & Akaho, S. (2011). Dimension reduction for object ranking. In Preference Learning (pp. 203–215). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-14125-6_10
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