计算机科学与探索2019，Vol.13Issue(8)：1272-1279,8.DOI:10.3778/j.issn.1673-9418.1809008
异常值自识别的低秩矩阵补全方法*
Low-Rank Matrix Completion with Self-Identification of Outliers*
摘要
Abstract
The problem of low-rank matrix completion has attracted massive attention in various engineering fields such as machine learning, image processing and video denoising. Assuming the data are low-rank, missing entries can be estimated in matrices by matrix completion algorithms, and the best approximation which fixes the constraints is produced. However, most matrix completion methods usually result in unsatisfactory reconstruction accuracy with adding non-Gaussian noise. This paper proposes a new robust algorithm for matrix completion considering some traditional methods which can enhance the robustness and avoid fitting. This paper can identify the location of outliers and replace them with approximate data, reducing the influence of outliers to the result, and enhancing the accuracy of the reconstruction. Simulated data and real data show that the algorithm is good at robustness and accuracy when the data sets are polluted by the non-Gaussian noise.关键词
矩阵补全/低秩矩阵恢复/异常值/鲁棒性Key words
matrix completion/low-rank matrix recovery/outliers/robustness分类
信息技术与安全科学引用本文复制引用
李可欣,徐彬,高克宁..异常值自识别的低秩矩阵补全方法*[J].计算机科学与探索,2019,13(8):1272-1279,8.基金项目
The Online Education Foundation of Ministry of Education Research Center under Grant No. 2016YB124 (教育部在线教育研究基金) （教育部在线教育研究基金）
the National Natural Science Foundation of China under Grant No. 51607029 (国家自然科学基金青年基金) （国家自然科学基金青年基金）
the Natural Science Foundation of Liaoning Province under Grant No. 2015020018 (辽宁省自然科学基金). （辽宁省自然科学基金）
