<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p><p>We analyze and evaluate our methods on several different synthetic and real-world datasets.</p></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.

Dimension Reduction for Objects Composed of Vector Sets

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Dimension Reduction for Objects Composed of Vector Sets"}

Dimension reduction and feature selection are fundamental tools for machine learning and data mining. Most existing methods, however, assume that objects are...

Dimension Reduction for Objects Composed of Vector Sets

Publié en ligne: 04 mai 2017

Pages: 169 - 180

Reçu: 04 mars 2016

Accepté: 06 oct. 2016

DOI: https://doi.org/10.1515/amcs-2017-0012

Mots clés
dimension reduction, discriminant analysis, object recognition, registration

© by Marton Szemenyei

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Dimension Reduction for Objects Composed of Vector Sets

Publié en ligne: 04 mai 2017

Pages: 169 - 180

Reçu: 04 mars 2016

Accepté: 06 oct. 2016

DOI: https://doi.org/10.1515/amcs-2017-0012

Mots clésdimension reduction, discriminant analysis, object recognition, registration

© by Marton Szemenyei

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
dimension reduction, discriminant analysis, object recognition, registration