2012 MultiSourceLearningforJointAnal

• (Yuan et al., 2012) ⇒ Lei Yuan, Yalin Wang, Paul M. Thompson, Vaibhav A. Narayan, and Jieping Ye. (2012). “Multi-source Learning for Joint Analysis of Incomplete Multi-modality Neuroimaging Data.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339710

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Multi-source+Learning+for+Joint+Analysis+of+Incomplete+Multi-modality+Neuroimaging+Data
• http://dl.acm.org/citation.cfm?id=2339530.2339710&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; incomplete data; multi-source feature learning; multi-task learning

Abstract

Incomplete data present serious problems when integrating large-scale 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 prediction scores; we then estimate the missing prediction scores, which, combined with the existing prediction scores, are used to build a multi-source fusion model. To illustrate the proposed approaches, we classify patients from the ADNI study into groups with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI's 780 participants (172 AD, 397 MCI, 211 Normal), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithms. Comprehensive experiments show that our proposed methods yield stable and promising results.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 MultiSourceLearningforJointAnal	Lei Yuan Jieping Ye Yalin Wang Vaibhav A. Narayan Paul M. Thompson			Multi-source Learning for Joint Analysis of Incomplete Multi-modality Neuroimaging Data				10.1145/2339530.2339710		2012