Population Value Decomposition, a Framework for the Analysis of Image Populations
Images, often stored in multidimensional arrays, are fast becoming ubiquitous in medical and public health research. Analyzing populations of images is a statistical problem that raises a host of daunting challenges. The most significant challenge is the massive size of the datasets incorporating im...
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| Vydané v: | Journal of the American Statistical Association Ročník 106; číslo 495; s. 775 - 790 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Alexandria, VA
American Statistical Association
01.09.2011
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| ISSN: | 0162-1459, 1537-274X |
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| Abstract | Images, often stored in multidimensional arrays, are fast becoming ubiquitous in medical and public health research. Analyzing populations of images is a statistical problem that raises a host of daunting challenges. The most significant challenge is the massive size of the datasets incorporating images recorded for hundreds or thousands of subjects at multiple visits. We introduce the population value decomposition (PVD), a general method for simultaneous dimensionality reduction of large populations of massive images. We show how PVD can be seamlessly incorporated into statistical modeling, leading to a new, transparent, and rapid inferential framework. Our PVD methodology was motivated by and applied to the Sleep Heart Health Study, the largest community-based cohort study of sleep containing more than 85 billion observations on thousands of subjects at two visits. This article has supplementary material online. |
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| AbstractList | Images, often stored in multidimensional arrays, are fast becoming ubiquitous in medical and public health research. Analyzing populations of images is a statistical problem that raises a host of daunting challenges. The most significant challenge is the massive size of the datasets incorporating images recorded for hundreds or thousands of subjects at multiple visits. We introduce the population value decomposition (PVD), a general method for simultaneous dimensionality reduction of large populations of massive images. We show how PVD can be seamlessly incorporated into statistical modeling, leading to a new, transparent, and rapid inferential framework. Our PVD methodology was motivated by and applied to the Sleep Heart Health Study, the largest community-based cohort study of sleep containing more than 85 billion observations on thousands of subjects at two visits. This article has supplementary material online. Images, often stored in multidimensional arrays, are fast becoming ubiquitous in medical and public health research. Analyzing populations of images is a statistical problem that raises a host of daunting challenges. The most significant challenge is the massive size of the datasets incorporating images recorded for hundreds or thousands of subjects at multiple visits. We introduce the population value decomposition (PVD), a general method for simultaneous dimensionality reduction of large populations of massive images. We show how PVD can be seamlessly incorporated into statistical modeling, leading to a new, transparent, and rapid inferential framework. Our PVD methodology was motivated by and applied to the Sleep Heart Health Study, the largest community-based cohort study of sleep containing more than 85 billion observations on thousands of subjects at two visits. This article has supplementary material online.Images, often stored in multidimensional arrays, are fast becoming ubiquitous in medical and public health research. Analyzing populations of images is a statistical problem that raises a host of daunting challenges. The most significant challenge is the massive size of the datasets incorporating images recorded for hundreds or thousands of subjects at multiple visits. We introduce the population value decomposition (PVD), a general method for simultaneous dimensionality reduction of large populations of massive images. We show how PVD can be seamlessly incorporated into statistical modeling, leading to a new, transparent, and rapid inferential framework. Our PVD methodology was motivated by and applied to the Sleep Heart Health Study, the largest community-based cohort study of sleep containing more than 85 billion observations on thousands of subjects at two visits. This article has supplementary material online. |
| Author | Crainiceanu, Ciprian M. Caffo, Brian S. Luo, Sheng Zipunnikov, Vadim M. Punjabi, Naresh M. |
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| Cites_doi | 10.1001/archinte.152.3.538 10.1016/S0893-6080(00)00026-5 10.1164/ajrccm.155.5.9154863 10.1016/j.neuroimage.2010.02.081 10.1002/hbm.1048 10.1016/j.neuroimage.2008.10.057 10.1198/016214504000001745 10.1183/09031936.98.12061264 10.1093/sleep/21.7.749 10.1016/0165-1684(94)90029-9 10.1137/0905052 10.1378/chest.94.1.32 10.1214/08-AOAS206 10.1214/088342306000000682 10.1093/biostatistics/kxp058 10.1198/jasa.2009.0020 10.1198/jasa.2009.tm08564 10.1093/sleep/21.7.759 |
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| Keywords | Biometrics Decomposition method Population analysis Decomposition Electroencephalography Statistical estimation Signal extraction Multivariate analysis Stochastic process Statistical method Random field Computational geometry Image analysis Reduction method Medical science Cohort study Computer graphics Public health |
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| References | p_17 Wold S. (p_28) 1984; 5 p_2 p_4 p_12 p_3 p_13 Redline S. (p_22) 1998; 21 p_6 p_25 p_7 p_9 Crainiceanu C. M. (p_8) 2009 p_30 Martin S. E. (p_19) 1997; 155 p_10 Di C. (p_11) 2009; 3 Karhunen K. (p_16) 1947; 37 Caffo B. S. (p_1) 2010; 51 Whitney C. W. (p_26) 1998; 21 Quan S. (p_20) 1997; 20 |
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| Title | Population Value Decomposition, a Framework for the Analysis of Image Populations |
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