Tensor factorization for missing data imputation in medical questionnaires
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 ut...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
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2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/98569 http://hdl.handle.net/10220/13419 |
| _version_ | 1826119285588623360 |
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| author | Dauwels, Justin Garg, Lalit Earnest, Arul Pang, Leong Khai |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Dauwels, Justin Garg, Lalit Earnest, Arul Pang, Leong Khai |
| author_sort | Dauwels, Justin |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:57:44Z |
| format | Conference Paper |
| id | ntu-10356/98569 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:57:44Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/985692020-03-07T13:24:48Z Tensor factorization for missing data imputation in medical questionnaires Dauwels, Justin Garg, Lalit Earnest, Arul Pang, Leong Khai School of Electrical and Electronic Engineering IEEE International Conference on Acoustics, Speech and Signal Processing (2012 : Kyoto, Japan) DRNTU::Engineering::Electrical and electronic engineering 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. 2013-09-09T07:52:30Z 2019-12-06T19:56:58Z 2013-09-09T07:52:30Z 2019-12-06T19:56:58Z 2012 2012 Conference Paper https://hdl.handle.net/10356/98569 http://hdl.handle.net/10220/13419 10.1109/ICASSP.2012.6288327 en © 2012 IEEE |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Dauwels, Justin Garg, Lalit Earnest, Arul Pang, Leong Khai Tensor factorization for missing data imputation in medical questionnaires |
| title | Tensor factorization for missing data imputation in medical questionnaires |
| title_full | Tensor factorization for missing data imputation in medical questionnaires |
| title_fullStr | Tensor factorization for missing data imputation in medical questionnaires |
| title_full_unstemmed | Tensor factorization for missing data imputation in medical questionnaires |
| title_short | Tensor factorization for missing data imputation in medical questionnaires |
| title_sort | tensor factorization for missing data imputation in medical questionnaires |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/98569 http://hdl.handle.net/10220/13419 |
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