LTSA algorithm for Dimension Reduction of Microarray Data
Dimension reduction is an important issue to understand microarray data.In this study,we proposed a efficient approach for dimensionality reduction ofmicroarray data.Our method allows to apply the manifold learning algorithm to analyses dimensionality reduction of microarray data.The intra-/inter-category distances were used as the criteria to quantitatively evaluate the effects of data dimensionality reduction.Colon cancer and leukaemia gene expression datasets are selected for our investigation.When the neighborhood parameter was effectivly set,all the intrinsic dimension numbers of data sets were low.Therefore,manifold learning is used to study microarray data in the low-dimensional projection space.Our results indicate that Manifold learning method possesses better effects than the linear methods in analysis of microarray data,which is suitable for clinical diagnosis and other medical applications.
Manifold learning dimensional reduction microarray data
XiaoZhou Chen
Modern Biology Research Center, Yunnan University, Kunming 650091,China;;School of Mathematics and Computer Science, Yunnan University of Nationalities,Kunming 650031 ,China
国际会议
太原
英文
192-195
2013-01-13（万方平台首次上网日期，不代表论文的发表时间）