Ordinal regularized manifold feature extraction for image ranking
Signal Processing
Feature selection for ordinal text classification
Neural Computation
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Ordered lists of objects are widely used as representational forms. Such ordered objects include Web search results and best-seller lists. Techniques for processing such ordinal data are being developed, particularly methods for a supervised ordering 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 a supervised ordering task.