Copy the page URI to the clipboard
Enki, Doyo G. and Trendafilov, Nickolay T.
(2012).
DOI: https://doi.org/10.1007/s00180-011-0280-2
Abstract
A cluster-based method for constructing sparse principal components is proposed. The method initially forms clusters of variables, using a new clustering approach called the semi-partition, in two steps. First, the variables are ordered sequentially according to a criterion involving the correlations between variables. Then, the ordered variables are split into two parts based on their generalized variance. The first group of variables becomes an output cluster, while the second one—input for another run of the sequential process. After the optimal clusters have been formed, sparse components are constructed from the singular value decomposition of the data matrices of each cluster. Themethod is applied to simple data setswith smaller number of variables (p) than observations (n), as well as large gene expression data sets with p » n. The resulting cluster-based sparse principal components are very promising as evaluated by objective criteria. The method is also compared with other existing approaches and is found to perform well.
Viewing alternatives
Metrics
Public Attention
Altmetrics from AltmetricNumber of Citations
Citations from Dimensions- Request a copy from the author This file is not available for public download