TY - JOUR
T1 - Spatially Weighted Principal Component Regression for high-dimensional prediction
AU - Shen, Dan
AU - Zhu, Hongtu
N1 - Funding Information:
This work was partially supported by the Startup Fund of University of South Florida, NIH grants MH086633, RR025747, and MH092335 and NSF grants SES-1357666 and DMS-1407655.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - We consider the problem of using high dimensional data residing on graphs to predict a low-dimensional outcome variable, such as disease status. Examples of data include time series and genetic data measured on linear graphs and imaging data measured on triangulated graphs (or lattices), among many others.Many of these data have two key features including spatial smoothness and intrinsically low dimensional structure. We propose a simple solution based on a general statistical framework, called spatially weighted principal component regression (SWPCR). In SWPCR, we introduce two sets of weights including importance score weights for the selection of individual features at each node and spatial weights for the incorporation of the neighboring pattern on the graph. We integrate the importance score weights with the spatial weights in order to recover the low dimensional structure of high dimensional data.We demonstrate the utility of our methods through extensive simulations and a real data analysis based on Alzheimer’s disease neuroimaging initiative data.
AB - We consider the problem of using high dimensional data residing on graphs to predict a low-dimensional outcome variable, such as disease status. Examples of data include time series and genetic data measured on linear graphs and imaging data measured on triangulated graphs (or lattices), among many others.Many of these data have two key features including spatial smoothness and intrinsically low dimensional structure. We propose a simple solution based on a general statistical framework, called spatially weighted principal component regression (SWPCR). In SWPCR, we introduce two sets of weights including importance score weights for the selection of individual features at each node and spatial weights for the incorporation of the neighboring pattern on the graph. We integrate the importance score weights with the spatial weights in order to recover the low dimensional structure of high dimensional data.We demonstrate the utility of our methods through extensive simulations and a real data analysis based on Alzheimer’s disease neuroimaging initiative data.
KW - Graph
KW - Principal component analysis
KW - Regression
KW - Spatial
KW - Supervise
KW - Weight
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U2 - 10.1007/978-3-319-19992-4_60
DO - 10.1007/978-3-319-19992-4_60
M3 - Conference article
C2 - 26213452
AN - SCOPUS:84983627568
SN - 0302-9743
VL - 9123
SP - 758
EP - 769
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 24th International Conference on Information Processing in Medical Imaging, IPMI 2015
Y2 - 28 June 2015 through 3 July 2015
ER -