Manifold regularized multiple kernel learning with Hellinger distance
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU134891" target="_blank" >RIV/00216305:26220/19:PU134891 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10586-018-2106-2" target="_blank" >https://link.springer.com/article/10.1007/s10586-018-2106-2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10586-018-2106-2" target="_blank" >10.1007/s10586-018-2106-2</a>
Alternative languages
Result language
angličtina
Original language name
Manifold regularized multiple kernel learning with Hellinger distance
Original language description
The aim of this paper is to solve the problem of unsupervised manifold regularization being used under supervised classification circumstance. This paper not only considers that the manifold information of data can provide useful information but also proposes a supervised method to compute the Laplacian graph by using the label information and the Hellinger distance for a comprehensive evaluation of the similarity of data samples. Meanwhile, multi-source or complex data is increasing nowadays. It is desirable to learn from several kernels that are adaptable and flexible to deal with this type of data. Therefore, our classifier is based on multiple kernel learning, and the proposed approach to supervised classification is a multiple kernel model with manifold regularization to incorporate intrinsic geometrical information. Finally, a classifier that minimizes the testing error and considers the geometrical structure of data is put forward. The results of experiments with other methods show the effectiveness of the proposed model and computing the inner potential geometrical information is useful for classification.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Cluster Computing-The Journal of Networks Software Tools and Applications
ISSN
1386-7857
e-ISSN
1573-7543
Volume of the periodical
22
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
13843-13851
UT code for WoS article
000501745700079
EID of the result in the Scopus database
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