http://scholars.ntou.edu.tw/handle/123456789/6030
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Chin-Chun Chang | en_US |
dc.contributor.author | Po-Yi Lin | en_US |
dc.date.accessioned | 2020-11-19T11:56:34Z | - |
dc.date.available | 2020-11-19T11:56:34Z | - |
dc.date.issued | 2015-03 | - |
dc.identifier.issn | 0893-6080 | - |
dc.identifier.uri | http://scholars.ntou.edu.tw/handle/123456789/6030 | - |
dc.description.abstract | The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Neural Networks | en_US |
dc.subject | Active learning | en_US |
dc.subject | Semi-supervised clustering | en_US |
dc.subject | Manifold learning | en_US |
dc.subject | Locally linear embedding | en_US |
dc.title | Active learning for semi-supervised clustering based on locally linear propagation reconstruction | en_US |
dc.type | journal article | en_US |
dc.identifier.doi | 10.1016/j.neunet.2014.11.006 | - |
dc.identifier.isi | WOS:000349730800016 | - |
dc.relation.journalvolume | 63 | en_US |
dc.relation.pages | 170-184 | en_US |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.fulltext | no fulltext | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.openairetype | journal article | - |
crisitem.author.dept | College of Electrical Engineering and Computer Science | - |
crisitem.author.dept | Department of Computer Science and Engineering | - |
crisitem.author.dept | National Taiwan Ocean University,NTOU | - |
crisitem.author.parentorg | National Taiwan Ocean University,NTOU | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
顯示於: | 資訊工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。