TY - JOUR
T1 - Spatially Weighted Principal Component Analysis for Imaging Classification
AU - Guo, Ruixin
AU - Ahn, Mihye
AU - Hongtu Zhu, Hongtu Zhu
N1 - Publisher Copyright:
© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
PY - 2015/1/2
Y1 - 2015/1/2
N2 - The aim of this article is to develop a supervised dimension-reduction framework, called spatially weighted principal component analysis (SWPCA), for high-dimensional imaging classification. Two main challenges in imaging classification are the high dimensionality of the feature space and the complex spatial structure of imaging data. In SWPCA, we introduce two sets of novel weights, including global and local spatial weights, which enable a selective treatment of individual features and incorporation of the spatial structure of imaging data and class label information. We develop an efficient two-stage iterative SWPCA algorithm and its penalized version along with the associated weight determination. We use both simulation studies and real data analysis to evaluate the finite-sample performance of our SWPCA. The results show that SWPCA outperforms several competing principal component analysis (PCA) methods, such as supervised PCA (SPCA), and other competing methods, such as sparse discriminant analysis (SDA).
AB - The aim of this article is to develop a supervised dimension-reduction framework, called spatially weighted principal component analysis (SWPCA), for high-dimensional imaging classification. Two main challenges in imaging classification are the high dimensionality of the feature space and the complex spatial structure of imaging data. In SWPCA, we introduce two sets of novel weights, including global and local spatial weights, which enable a selective treatment of individual features and incorporation of the spatial structure of imaging data and class label information. We develop an efficient two-stage iterative SWPCA algorithm and its penalized version along with the associated weight determination. We use both simulation studies and real data analysis to evaluate the finite-sample performance of our SWPCA. The results show that SWPCA outperforms several competing principal component analysis (PCA) methods, such as supervised PCA (SPCA), and other competing methods, such as sparse discriminant analysis (SDA).
KW - PCA
KW - Spatial weight
UR - http://www.scopus.com/inward/record.url?scp=84926183144&partnerID=8YFLogxK
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U2 - 10.1080/10618600.2014.912135
DO - 10.1080/10618600.2014.912135
M3 - Article
C2 - 26089629
AN - SCOPUS:84926183144
SN - 1061-8600
VL - 24
SP - 274
EP - 296
JO - Journal of Computational and Graphical Statistics
JF - Journal of Computational and Graphical Statistics
IS - 1
ER -