摘要
对低秩矩阵中的存在的鲁棒性主成分分析进行综述,并分析该存在的优化模型及其优化算法,并分析这些不同的优化方法存在的优缺点以及可能的应用领域,最后指出该模型进一步的研究方向。
in this paper,the robust principal component analysis(pca)in low-rank matrices is summarized,and the optimization model and algorithm of the existence are analyzed.The advantages and disadvantages of these different optimization methods and their possible application fields are analyzed.Finally,the further research direction of the model is pointed out.
出处
《数码设计》
2019年第18期41-42,共2页
Peak Data Science
关键词
鲁棒性主成分分析
交替方向乘子法
迭代阈值算法
加速近端梯度法
robust principal component analysis
Alternating direction multiplier method
Iterative threshold algorithm
Accelerated proximal gradient method
