PUBLICATION DATE

2016-04-27

AUTHORS

Changming Zhu

TITLE

Double-fold localized multiple matrix learning machine with Universum

ISSUE

N/A

VOLUME

N/A

ISSN (print)

1433-7541

ISSN (electronic)

1433-755X

ABSTRACT

Matrix learning, multiple-view learning, Universum learning, and local learning are four hot spots of present research. Matrix learning aims to design feasible machines to process matrix patterns directly. Multiple-view learning takes pattern information from multiple aspects, i.e., multiple-view information into account. Universum learning can reflect priori knowledge about application domain and improve classification performances. A good local learning approach is important to the finding of local structures and pattern information. Our previous proposed learning machine, double-fold localized multiple matrix learning machine is a one with multiple-view information, local structures, and matrix learning. But this machine does not take Universum learning into account. Thus, this paper proposes a double-fold localized multiple matrix learning machine with Universum (Uni-DLMMLM) so as to improve the performance of a learning machine. Experimental results have validated that Uni-DLMMLM (1) makes full use of the domain knowledge of whole data distribution as well as inherits the advantages of matrix learning; (2) combines Universum learning with matrix learning so as to capture more global knowledge; (3) has a good ability to process different kinds of data sets; (4) has a superior classification performance and leads to a low empirical generation risk bound.

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26 TRIPLES      24 PREDICATES      27 URIs      16 LITERALS

Subject Predicate Object
1 articles:5f614ce927ad0937e9d5ce1622e60a78 sg:abstract Abstract Matrix learning, multiple-view learning, Universum learning, and local learning are four hot spots of present research. Matrix learning aims to design feasible machines to process matrix patterns directly. Multiple-view learning takes pattern information from multiple aspects, i.e., multiple-view information into account. Universum learning can reflect priori knowledge about application domain and improve classification performances. A good local learning approach is important to the finding of local structures and pattern information. Our previous proposed learning machine, double-fold localized multiple matrix learning machine is a one with multiple-view information, local structures, and matrix learning. But this machine does not take Universum learning into account. Thus, this paper proposes a double-fold localized multiple matrix learning machine with Universum (Uni-DLMMLM) so as to improve the performance of a learning machine. Experimental results have validated that Uni-DLMMLM (1) makes full use of the domain knowledge of whole data distribution as well as inherits the advantages of matrix learning; (2) combines Universum learning with matrix learning so as to capture more global knowledge; (3) has a good ability to process different kinds of data sets; (4) has a superior classification performance and leads to a low empirical generation risk bound.
2 sg:articleType OriginalPaper
3 sg:ddsId s10044-016-0548-9
4 sg:ddsIdJournalBrand 10044
5 sg:doi 10.1007/s10044-016-0548-9
6 sg:doiLink http://dx.doi.org/10.1007/s10044-016-0548-9
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12 sg:hasJournalBrand journal-brands:eecfead65c5404d7ccf7be6b46baade3
13 sg:issnElectronic 1433-755X
14 sg:issnPrint 1433-7541
15 sg:language English
16 sg:license http://scigraph.springernature.com/explorer/license/
17 sg:pageEnd 28
18 sg:pageStart 1
19 sg:publicationDate 2016-04-27
20 sg:publicationYear 2016
21 sg:publicationYearMonth 2016-04
22 sg:scigraphId 5f614ce927ad0937e9d5ce1622e60a78
23 sg:title Double-fold localized multiple matrix learning machine with Universum
24 sg:webpage https://link.springer.com/10.1007/s10044-016-0548-9
25 rdf:type sg:Article
26 rdfs:label Article: Double-fold localized multiple matrix learning machine with Universum
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