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Spatially Weighted Principal Component Regression for High-dimensional Prediction
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...
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| 出版年: | Inf Process Med Imaging |
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| 主要な著者: | , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2015
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4511401/ https://ncbi.nlm.nih.gov/pubmed/26213452 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/978-3-319-19992-4_60 |
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