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Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data by Ayhan, Murat Seckin; Raghavan, Vijay; Initiative, Alzheimer's disease Neuroimaging is a scholarly article available to read on EtoBox.

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Alzheimer's disease is a major cause of dementia. Its diagnosis requires accurate biomarkers that are sensitive to disease stages. In this respect, we regard probabilistic classification as a method of designing a probabilistic biomarker for disease staging. Probabilistic biomarkers naturally support the interpretation of decisions and evaluation of uncertainty associated with them. In this paper, we obtain probabilistic biomarkers via Gaussian Processes. Gaussian Processes enable probabilistic kernel machines that offer flexible means to accomplish Multiple Kernel Learning. Exploiting this flexibility, we propose a new variation of Automatic Relevance Determination and tackle the challenges of high dimensionality through multiple kernels. Our research results demonstrate that the Gaussian Process models are competitive with or better than the well-known Support Vector Machine in terms of classification performance even in the cases of single kernel learning. Extending the basic scheme towards the Multiple Kernel Learning, we improve the efficacy of the Gaussian Process models and their interpretability in terms of the known anatomical correlates of the disease. For instance, the d

Author

Ayhan, Murat Seckin; Raghavan, Vijay; Initiative, Alzheimer's disease Neuroimaging

Published

2017

Language

EN