Manifold regularized multiple kernel learning with Hellinger distance

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Manifold regularized multiple kernel learning with Hellinger distance

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Manifold regularized multiple kernel learning with Hellinger distance

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Cluster Computing-The Journal of Networks Software Tools and Applications

ISSN

1386-7857

e-ISSN

1573-7543

Svazek periodika

22

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

13843-13851

Kód UT WoS článku

000501745700079

EID výsledku v databázi Scopus

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