PUBLICATION DATE

2016-06-22

AUTHORS

Nan Zhang, Shifei Ding

TITLE

Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data

ISSUE

2

VOLUME

9

ISSN (print)

1865-9284

ISSN (electronic)

1865-9292

ABSTRACT

Extreme learning machine (ELM) not only is an effective classifier in supervised learning, but also can be applied on unsupervised learning and semi-supervised learning. The model structure of unsupervised extreme learning machine (US-ELM) and semi-supervised extreme learning machine (SS-ELM) are same as ELM, the difference between them is the cost function. We introduce kernel function to US-ELM and propose unsupervised extreme learning machine with kernel (US-KELM). And SS-KELM has been proposed. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and Wavelet kernel functions have been widely used in support vector machine. Therefore, to realize a combination of the wavelet kernel function, US-ELM, and SS-ELM, unsupervised extreme learning machine with wavelet kernel function (US-WKELM) and semi-supervised extreme learning machine with wavelet kernel function (SS-WKELM) are proposed in this paper. The experimental results show the feasibility and validity of US-WKELM and SS-WKELM in clustering and classification.

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34 TRIPLES      30 PREDICATES      35 URIs      22 LITERALS

Subject Predicate Object
1 articles:a50ea1d06f59f1934e538f8ea485f425 sg:abstract Abstract Extreme learning machine (ELM) not only is an effective classifier in supervised learning, but also can be applied on unsupervised learning and semi-supervised learning. The model structure of unsupervised extreme learning machine (US-ELM) and semi-supervised extreme learning machine (SS-ELM) are same as ELM, the difference between them is the cost function. We introduce kernel function to US-ELM and propose unsupervised extreme learning machine with kernel (US-KELM). And SS-KELM has been proposed. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and Wavelet kernel functions have been widely used in support vector machine. Therefore, to realize a combination of the wavelet kernel function, US-ELM, and SS-ELM, unsupervised extreme learning machine with wavelet kernel function (US-WKELM) and semi-supervised extreme learning machine with wavelet kernel function (SS-WKELM) are proposed in this paper. The experimental results show the feasibility and validity of US-WKELM and SS-WKELM in clustering and classification.
2 sg:articleType OriginalPaper
3 sg:coverYear 2017
4 sg:coverYearMonth 2017-06
5 sg:ddsId s12293-016-0198-x
6 sg:ddsIdJournalBrand 12293
7 sg:doi 10.1007/s12293-016-0198-x
8 sg:doiLink http://dx.doi.org/10.1007/s12293-016-0198-x
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19 sg:issnElectronic 1865-9292
20 sg:issnPrint 1865-9284
21 sg:issue 2
22 sg:language English
23 sg:license http://scigraph.springernature.com/explorer/license/
24 sg:pageEnd 139
25 sg:pageStart 129
26 sg:publicationDate 2016-06-22
27 sg:publicationYear 2016
28 sg:publicationYearMonth 2016-06
29 sg:scigraphId a50ea1d06f59f1934e538f8ea485f425
30 sg:title Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data
31 sg:volume 9
32 sg:webpage https://link.springer.com/10.1007/s12293-016-0198-x
33 rdf:type sg:Article
34 rdfs:label Article: Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data
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