To solve the clinical data recover problem of autism spectrum disorders (ASDs), a high-rank matrix completion (HRMC) algorithm based on alternating direction method of multipliers (ADMM) was proposed. Under consideration of different parameters with different significance, the important parameters and unimportant ones were attached with various weights. In a case study, test data were generated to find the optimal parameters. Furthermore, the algorithm was applied on practical ASD clinical data. The results show that the algorithm performs better in comparison with other parameterized algorithms and normal matrix completion algorithm, which indicates that it can be applied in practical data cleaning and processing.
LI Yuanchao,CHEN Feng
. High-Rank Matrix Completion Method for
Autism Spectrum Disorders[J]. Journal of Shanghai Jiaotong University, 2019
, 53(6)
: 734
-740
.
DOI: 10.16183/j.cnki.jsjtu.2019.06.015
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