基于秩的逼近的张量鲁棒主成分分析
Tensor Robust Principal Component Analysis via Non-Convex Rank Approximation
摘要:
本文提出来一种新的张量秩近似并建立一种非凸TRPCA模型，使用一种有效的增广拉格朗日乘子优化算法对这个非凸最小化问题进行求解。实验结果表明，相对于基于核范数的张量鲁棒主成分分析算法，该算法得到的估计张量的偏差更小，在精度和效率上是有效的。
Abstract:
In this paper, a new tensor rank approximation is proposed and a non-convex TRPCA model is established. An effective augmented Lagrangian multiplier optimization algorithm is used to solve this non-convex minimization problem. The experimental results show that compared with the tensor robust principal component analysis algorithm based on kernel norm, the estimated tensor obtained by this algorithm has smaller deviation and is effective in terms of accuracy and efficiency.
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