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  • Multiple incomplete views clustering via non-negative matrix factorization with its application in Alzheimer's disease analysis
Title: Multiple incomplete views clustering via non-negative matrix factorization with its application in Alzheimer's disease analysis
Traditional neuroimaging analysis, such as clustering the data collected for the Alzheimer's disease (AD), usually relies on the data from one single imaging modality. However, recent technology and equipment advancements provide with us opportunities to better analyze diseases, where we could collect and employ the data from different image and genetic modalities that may potentially enhance the predictive performance. To perform better clustering in AD analysis, in this paper we conduct a new study to make use of the data from different modalities/views. To achieve this goal, we propose a simple yet efficient method based on Non-negative Matrix Factorization (NMF) which can not only achieve better prediction performance but also deal with some data missing in some views. Experimental results on the ADNI dataset demonstrate the effectiveness of our proposed method.  more » « less
Award ID(s):
1423591 1652943
PAR ID:
10129618
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Page Range / eLocation ID:
1402 to 1405
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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