Szemenyei, Marton ; Vajda, Ferenc
Contributor:Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Title:Dimension reduction for objects composed of vector sets
Group publication title: Subject and Keywords:dimension reduction ; discriminant analysis ; object recognition ; registration
Abstract: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. ; Moreover, some applications (such as pose estimation) may require the recognition of individual vectors (nodes) of an object. In such cases it is essential that the nodes within a single object remain distinguishable after dimension reduction. In this paper we propose new discriminant analysis methods that are able to satisfy two criteria at the same time: separating between classes and between the nodes of an object instance. We analyze and evaluate our methods on several different synthetic and real-world datasets.
Publisher:Zielona Góra: Uniwersytet Zielonogórski
Date: Resource Type: DOI: Pages: Source:AMCS, volume 27, number 1 (2017) ; click here to follow the link
Language: License CC BY 4.0: Rights: