@article {444,
	title = {Tensor graph-optimized linear discriminant analysis},
	journal = {Journal of Digital Information Management},
	volume = {12},
	year = {2014},
	note = {Export Date: 10 July 2014},
	month = {2014},
	pages = {31 - 35},
	abstract = {Graph-based Fisher Analysis (GbFA) is proposed recently for dimensionality reduction, which has the powerful discriminant ability. However, GbFA is based on the matrix-to-vector way, which not only costs much but also loses spatial relations of pixels in images. Therefore, Tensor Graph-based Linear Discriminant Analysis (TGbLDA) is proposed in the paper. TGbLDA regards samples as data in tensor space and gets projection matrixes through the iteration way. Besides, TGbLDA inherits merits of GbFA. Experiments on Yale and YaleB face datasets demonstrate the effectiveness of our proposed algorithm.},
	keywords = {Dimensionality reduction, Face recognition, Graph-based fisher discriminant analysis, Tensor data},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84900431732\&partnerID=40\&md5=cbccd93685522d83cd7e46df5728b797},
	author = {Chen, J.}
}
