Sparse principal components by semi-partition clustering
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. The method is applied to simple data sets with 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. Copyright Springer-Verlag 2012
Volume (Year): 27 (2012)
Issue (Month): 4 (December)
|Contact details of provider:|| Web page: http://www.springer.com|
|Order Information:||Web: http://www.springer.com/statistics/journal/180/PS2|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
- Valentin Rousson & Theo Gasser, 2004. "Simple component analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(4), pages 539-555.
When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:27:y:2012:i:4:p:605-626. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)or (Rebekah McClure)
If references are entirely missing, you can add them using this form.