Multiple Incomplete Views Clustering via Non-Negative Matrix Factorization with Its Application in Alzheimer's Disease Analysis
Kai Liu, Hua Wang, Shannon Risacher, Andrew Saykin, Li Shen, for the ADNI
ISBI - 2018
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.
Links
- View publications from Kai Liu
- View publications from Hua Wang
- View publications presented in ISBI
- View publications in the project, Mining Brain Imaging Genomics Data for Improved Cognitive Health
- View publications in the project, Mining Materials Genome Data for Prediction and Guidance of Nanoparticle Synthesis
- View publications researching Matrix/Tensor Factorization
- View publications applied to Medical Image Computing
Cite this paper
MLA
Liu, Kai, et al. "Multiple incomplete views clustering via non-negative matrix factorization with its application in Alzheimer's disease analysis." 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018.
BibTeX
@inproceedings{liu2018multiple,
title={Multiple incomplete views clustering via non-negative matrix factorization with its application in Alzheimer's disease analysis},
author={Liu, Kai and Wang, Hua and Risacher, Shannon and Saykin, Andrew and Shen, Li},
booktitle={2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
pages={1402--1405},
year={2018},
organization={IEEE}
}