关键词: 谱回归, 图正则最小二乘回归, 降维, 聚类

Abstract: 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.

Key words: spectral regression, graph regularization least square regression, dimension reduction, clustering