Dimension reduction for objects composed of vector sets - Szemenyei, Marton; Vajda, Ferenc (Zielona Góra: Uniwersytet Zielonogórski)
Dimension reduction and feature selection are fundamental tools for machine learning and data mining. Most existing methods, however, assume that objects are represented by a single vectorial descriptor. In reality, some description methods assign unordered sets or graphs of vectors to a single object, where each vector is assumed to have the same number of dimensions, but is drawn from a different probability distribution.
Keywords: dimension reduction; discriminant analysis; object recognition; registration