Autor:

Szemenyei, Marton ; Vajda, Ferenc

Współtwórca:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Tytuł:

Dimension reduction for objects composed of vector sets

Tytuł publikacji grupowej:

AMCS, Volume 27 (2017)

Temat i słowa kluczowe:

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2017

Typ zasobu:

artykuł

DOI:

10.1515/amcs-2017-0012

Strony:

169-180

Źródło:

AMCS, volume 27, number 1 (2017) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego