dc.contributor.authorNie, Feiping
dc.contributor.authorXiang, Shiming
dc.contributor.authorLiu, Yun
dc.contributor.authorHou, Chenping
dc.contributor.authorZhang, Changshui
dc.date.accessioned2013-11-11T05:39:15Z
dc.date.available2013-11-11T05:39:15Z
dc.date.copyright2011en_US
dc.date.issued2011
dc.identifier.citationNie, F., Xiang, S., Liu, Y., Hou, C., & Zhang, C. (2012). Orthogonal vs. uncorrelated least squares discriminant analysis for feature extraction. Pattern Recognition Letters, 33(5), 485-491.en_US
dc.identifier.issn0167-8655en_US
dc.identifier.urihttp://hdl.handle.net/10220/17576
dc.description.abstractIn this paper, a new discriminant analysis for feature extraction is derived from the perspective of least squares regression. To obtain great discriminative power between classes, all the data points in each class are expected to be regressed to a single vector, and the basic task is to find a transformation matrix such that the squared regression error is minimized. To this end, two least squares discriminant analysis methods are developed under the orthogonal or the uncorrelated constraint. We show that the orthogonal least squares discriminant analysis is an extension to the null space linear discriminant analysis, and the uncorrelated least squares discriminant analysis is exactly equivalent to the traditional linear discriminant analysis. Comparative experiments show that the orthogonal one is more preferable for real world applications.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesPattern recognition lettersen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering
dc.titleOrthogonal vs. uncorrelated least squares discriminant analysis for feature extractionen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.patrec.2011.11.028


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