%0 Journal Article
%A WENG Qian1
%A 2
%A 3
%A MAO Zhengyuan2
%A LIN Jiawen3
%A JIAN Cairen4
%T Dimension reduction method based on spectral regression and graph regularization least square regression
%D 2017
%R 10.3778/j.issn.1002-8331.1507-0300
%J Computer Engineering and Applications
%P 81-84
%V 53
%N 5
%X Data dimension reduction is significant to research high-dimensional data. Sparse concept coding receives widespread attention, but the sparse representation coefficients fail to maintain the essential structure of the data. In response to this discovery, a method based on spectral regression and graph regularization least square regression for data dimension reduction is proposed. The experiments on two image data sets and two gene expression data sets show the proposed method is better than the unimproved sparse concept coding.
%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1507-0300