Principal components analysis (PCA) has been widely used as a statistical tool for the dimension
reduction of multivariate data in various application areas and extensively studied
in the long history of statistics. One of the limitations of PCA machinery is that PCA can be
applied only to the continuous type variables. Recent advances of information technology
in various applied areas have created numerous large diverse data sets with a high dimensional
feature space, including high dimensional binary data. In spite of such great demands,
only a few methodologies tailored to such binary dataset have been suggested. The
methodologies we developed are the model-based approach for generalization to binary
data. We developed a statistical model for binary PCA and proposed two stable estimation
procedures using MM algorithm and variational method. By considering the regularization
technique, the selection of important variables is automatically achieved. We also proposed
an efficient algorithm for model selection including the choice of the number of principal
components and regularization parameter in this study.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-05-602 |
| Date | 2009 May 1900 |
| Creators | Lee, Seokho |
| Contributors | HUANG, JIANHUA Z., CARROLL, RAYMOND J. |
| Source Sets | Texas A and M University |
| Language | English |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | application/pdf |
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