PUBLICATION DATE

2015-02-28

AUTHORS

Bing Liu, Yong Zhou, Fan-Rong Meng, Shi-Xiong Xia

TITLE

Manifold regularized extreme learning machine

ISSUE

2

VOLUME

27

ISSN (print)

0941-0643

ISSN (electronic)

1433-3058

ABSTRACT

Extreme learning machine (ELM) works for generalized single-hidden-layer feedforward networks (SLFNs), and its essence is that the hidden layer of SLFNs need not be tuned. But ELM only utilizes labeled data to carry out the supervised learning task. In order to exploit unlabeled data in the ELM model, we first extend the manifold regularization (MR) framework and then demonstrate the relation between the extended MR framework and ELM. Finally, a manifold regularized extreme learning machine is derived from the proposed framework, which maintains the properties of ELM and can be applicable to large-scale learning problems. Experimental results show that the proposed semi-supervised extreme learning machine is the most cost-efficient method. It tends to have better scalability and achieve satisfactory generalization performance at a relatively faster learning speed than traditional semi-supervised learning algorithms.

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36 TRIPLES      30 PREDICATES      37 URIs      22 LITERALS

Subject Predicate Object
1 articles:e13340560bdd0b8c8e612e23b3b05cfe sg:abstract Abstract Extreme learning machine (ELM) works for generalized single-hidden-layer feedforward networks (SLFNs), and its essence is that the hidden layer of SLFNs need not be tuned. But ELM only utilizes labeled data to carry out the supervised learning task. In order to exploit unlabeled data in the ELM model, we first extend the manifold regularization (MR) framework and then demonstrate the relation between the extended MR framework and ELM. Finally, a manifold regularized extreme learning machine is derived from the proposed framework, which maintains the properties of ELM and can be applicable to large-scale learning problems. Experimental results show that the proposed semi-supervised extreme learning machine is the most cost-efficient method. It tends to have better scalability and achieve satisfactory generalization performance at a relatively faster learning speed than traditional semi-supervised learning algorithms.
2 sg:articleType OriginalPaper
3 sg:coverYear 2016
4 sg:coverYearMonth 2016-02
5 sg:ddsId s00521-014-1777-8
6 sg:ddsIdJournalBrand 521
7 sg:doi 10.1007/s00521-014-1777-8
8 sg:doiLink http://dx.doi.org/10.1007/s00521-014-1777-8
9 sg:hasContributingOrganization grid-institutes:grid.411510.0
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14 sg:hasFieldOfResearchCode anzsrc-for:08
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16 sg:hasJournal journals:3a21858bf4a51235186f9d33eccbe802
17 journals:db33d4382a496aa4231fc989e4b287bd
18 sg:hasJournalBrand journal-brands:238bb913fe5c5128c3967c4c4825ce63
19 sg:indexingDatabase Scopus
20 Web of Science
21 sg:issnElectronic 1433-3058
22 sg:issnPrint 0941-0643
23 sg:issue 2
24 sg:language English
25 sg:license http://scigraph.springernature.com/explorer/license/
26 sg:pageEnd 269
27 sg:pageStart 255
28 sg:publicationDate 2015-02-28
29 sg:publicationYear 2015
30 sg:publicationYearMonth 2015-02
31 sg:scigraphId e13340560bdd0b8c8e612e23b3b05cfe
32 sg:title Manifold regularized extreme learning machine
33 sg:volume 27
34 sg:webpage https://link.springer.com/10.1007/s00521-014-1777-8
35 rdf:type sg:Article
36 rdfs:label Article: Manifold regularized extreme learning machine
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