Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICASSP.2012.6288327
Title: Tensor factorization for missing data imputation in medical questionnaires
Authors: Dauwels, J.
Garg, L.
Earnest, A. 
Pang, L.K.
Keywords: Data handling
Health information management
Medical information systems
Public healthcare
Issue Date: 2012
Citation: Dauwels, J.,Garg, L.,Earnest, A.,Pang, L.K. (2012). Tensor factorization for missing data imputation in medical questionnaires. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings : 2109-2112. ScholarBank@NUS Repository. https://doi.org/10.1109/ICASSP.2012.6288327
Abstract: This paper presents innovative collaborative filtering techniques to complete missing data in repeated medical questionnaires. The proposed techniques are based on the canonical polyadic (CP) decomposition (a.k.a. PARAFAC). Besides the standard CP decomposition, also a normalized decomposition is utilized. As an illustration, systemic lupus erythematosus-specific quality-of-life questionnaire is considered. Measures such as normalized root mean square error, bias and variance are used to assess the performance of the proposed tensor-based methods in comparison with other widely used approaches, such as mean substitution, regression imputations and k-nearest neighbor estimation. The numerical results demonstrate that the proposed methods provide significant improvement in comparison to popular methods. The best results are obtained for the normalized decomposition. © 2012 IEEE.
Source Title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/110666
ISBN: 9781467300469
ISSN: 15206149
DOI: 10.1109/ICASSP.2012.6288327
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