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DC Field | Value | Language |
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dc.contributor.author | Dauwels, Justin | - |
dc.contributor.author | Garg, Lalit | - |
dc.contributor.author | Earnest, Arul | - |
dc.contributor.author | Pang, Leong Khai | - |
dc.date.accessioned | 2017-12-20T13:38:12Z | - |
dc.date.available | 2017-12-20T13:38:12Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | Dauwels, J., Garg, L., Earnest, A., & Pang, L. K. (2012). Tensor factorization for missing data imputation in medical questionnaires. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto. 2109-2112. | en_GB |
dc.identifier.isbn | 9781467300469 | - |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/24953 | - |
dc.description.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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Medical informatics | en_GB |
dc.subject | Health facilities | en_GB |
dc.subject | Medical care | en_GB |
dc.title | Tensor factorization for missing data imputation in medical questionnaires | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.bibliographicCitation.conferencename | 2012 IEEE International Conference on Acoustics, Speech and Signal Processing | en_GB |
dc.bibliographicCitation.conferenceplace | Kyoto, Japan, 25-30/03/2013 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/ICASSP.2012.6288327 | - |
Appears in Collections: | Scholarly Works - FacICTCIS |
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Tensor_factorization_for_missing_data_im.pdf Restricted Access | 1.03 MB | Adobe PDF | View/Open Request a copy |
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