dc.contributor.authorZhang, Hong.
dc.contributor.authorYu, Jun.
dc.contributor.authorWang, Meng.
dc.contributor.authorLiu, Yun.
dc.date.accessioned2013-09-24T07:33:03Z
dc.date.available2013-09-24T07:33:03Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationZhang, H., Yu, J., Wang, M., & Liu, Y. (2012). Semi-supervised distance metric learning based on local linear regression for data clustering. Neurocomputing, 93, 100-105.
dc.identifier.urihttp://hdl.handle.net/10220/13661
dc.description.abstractDistance metric plays an important role in many machine learning tasks. The distance between samples is mostly measured with a predefined metric, ignoring how the samples distribute in the feature space and how the features are correlated. This paper proposes a semi-supervised distance metric learning method by exploring feature correlations. Specifically, unlabeled samples are used to calculate the prediction error by means of local linear regression. Labeled samples are used to learn discriminative ability, that is, maximizing the between-class covariance and minimizing the within-class covariance. We then fuse the knowledge learned from both labeled and unlabeled samples into an overall objective function which can be solved by maximum eigenvectors. Our algorithm explores both labeled and unlabeled information as well as data distribution. Experimental results demonstrates the superiority of our method over several existing algorithms.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesNeurocomputingen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering
dc.titleSemi-supervised distance metric learning based on local linear regression for data clusteringen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.neucom.2012.03.007


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record