TY - DATA T1 - The Spatial LASSO With Applications to Unmixing Hyperspectral Biomedical Images PY - 2015/11/18 AU - Daniel V. Samarov AU - Jeeseong Hwang AU - Maritoni Litorja UR - https://tandf.figshare.com/articles/dataset/The_Spatial_LASSO_With_Applications_to_Unmixing_Hyperspectral_Biomedical_Images/1230086 DO - 10.6084/m9.figshare.1230086.v2 L4 - https://ndownloader.figshare.com/files/1780619 L4 - https://ndownloader.figshare.com/files/1780620 L4 - https://ndownloader.figshare.com/files/1780621 L4 - https://ndownloader.figshare.com/files/3243929 L4 - https://ndownloader.figshare.com/files/1780623 L4 - https://ndownloader.figshare.com/files/1780624 L4 - https://ndownloader.figshare.com/files/1780625 KW - Unmixing Hyperspectral Biomedical Images Hyperspectral imaging KW - tissue level KW - spectroscopic method KW - measurement KW - biomedicine KW - Supplementary materials KW - multivariate response data KW - SPLASSO KW - model estimation process KW - selection method KW - article KW - electromagnetic spectrum KW - Spatial LASSO KW - novel regularization KW - abundance fractions KW - benchmark HSI dataset N2 - Hyperspectral imaging (HSI) is a spectroscopic method that uses densely sampled measurements along the electromagnetic spectrum to identify the unique molecular composition of an object. Traditionally HSI has been associated with remote sensing-type applications, but recently has found increased use in biomedicine, from investigations at the cellular to the tissue level. One of the main challenges in the analysis of HSI is estimating the proportions, also called abundance fractions of each of the molecular signatures. While there is great promise for HSI in the area of biomedicine, large variability in the measurements and artifacts related to the instrumentation has slow adoption into more widespread practice. In this article, we propose a novel regularization and variable selection method called the spatial LASSO (SPLASSO). The SPLASSO incorporates spatial information via a graph Laplacian-based penalty to help improve the model estimation process for multivariate response data. We show the strong performance of this approach on a benchmark HSI dataset with considerable improvement in predictive accuracy over the standard LASSO. Supplementary materials for this article are available online. ER -