Dimension reduction for objects composed of vector sets

• Autorzy: Marton Szemenyei , Ferenc Vajda
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

dimension reduction; discriminant analysis; object recognition; registration

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2017
• Tom: 27
• Numer: 1
• Strony: 169-180

Daty

wydano

2017

otrzymano

2016-03-04

poprawiono

2016-08-10

poprawiono

2016-09-17

zaakceptowano

2016-10-06

Twórcy

autor

Marton Szemenyei

• Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Magyar tudosok krt. 2, 1117, Budapest, Hungary

autor

Ferenc Vajda

• Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Magyar tudosok krt. 2, 1117, Budapest, Hungary

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Identyfikatory

DOI

10.1515/amcs-2017-0012