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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages1402-1405
Number of pages4
Volume2018-April
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Other

Other15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/4/184/7/18

Fingerprint

Factorization
Cluster Analysis
Alzheimer Disease
Neuroimaging
Technology
Equipment and Supplies
Imaging techniques
Datasets

Keywords

  • Alzheimer's Disease (AD)
  • Incomplete Views
  • Multi-View Clustering
  • Non-negative Matrix Factorization

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Liu, K., Wang, H., Risacher, S., Saykin, A., & Shen, L. (2018). Multiple incomplete views clustering via non-negative matrix factorization with its application in Alzheimer's disease analysis. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (Vol. 2018-April, pp. 1402-1405). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363834

Multiple incomplete views clustering via non-negative matrix factorization with its application in Alzheimer's disease analysis. / Liu, Kai; Wang, Hua; Risacher, Shannon; Saykin, Andrew; Shen, Li.

2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April IEEE Computer Society, 2018. p. 1402-1405.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liu, K, Wang, H, Risacher, S, Saykin, A & Shen, L 2018, Multiple incomplete views clustering via non-negative matrix factorization with its application in Alzheimer's disease analysis. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. vol. 2018-April, IEEE Computer Society, pp. 1402-1405, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 4/4/18. https://doi.org/10.1109/ISBI.2018.8363834
Liu K, Wang H, Risacher S, Saykin A, Shen L. Multiple incomplete views clustering via non-negative matrix factorization with its application in Alzheimer's disease analysis. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April. IEEE Computer Society. 2018. p. 1402-1405 https://doi.org/10.1109/ISBI.2018.8363834
Liu, Kai ; Wang, Hua ; Risacher, Shannon ; Saykin, Andrew ; Shen, Li. / 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. Vol. 2018-April IEEE Computer Society, 2018. pp. 1402-1405
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