期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 19, 期 4, 页码 930-946出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/jcgs.2010.08127
关键词
Fusion penalty; Local quadratic approximation; Sparsity; Variable selection
资金
- NSF [DMS-0505424, DMS-0805798, DMS-0505432, DMS-0705532, DMS-0748389]
- NIH [1RC1CA145444-0110]
- MEDC [GR-687]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0906784] Funding Source: National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0805798] Funding Source: National Science Foundation
In this article, we propose a new method for principal component analysis (PCA), whose main objective is to capture natural blocking structures in the variables. Further, the method, beyond selecting different variables for different components, also encourages the loadings of highly correlated variables to have the same magnitude. These two features often help in interpreting the principal components. To achieve these goals, a fusion penalty is introduced and the resulting optimization problem solved by an alternating block optimization algorithm. The method is applied to a number of simulated and real datasets and it is shown that it achieves the stated objectives. The supplemental materials for this article are available online.
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