Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data

Incomplete data present serious problems when integrating largescale brain imaging data sets from different imaging modalities. In the Alzheimer’s Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. We address this problem by proposing two novel learning methods where all the samples (with at least one available data source) can be used. In the first method, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. Our second method learns a base classifier for each data source independently, based on which we represent each source using a single column of p...

• Type: Journal
• Year: 2012
• Where: KDD
• Authors: Lei Yuan, Yalin Wang, Paul M. Thompson, Vaibhav A. Narayan, Jieping Ye
• Keywords: Data Mining, KDD 2012, Magnetic Resonance Imaging, Magnetic Resonance Imaging Mri, Mild Cognitive Impairment