标题： 基于纤维CUR分解的快速鲁棒张量主成分分析 显示英文标题

标题： Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition

摘要： 我们研究张量鲁棒主成分分析（TRPCA）问题，其目的是从它们的和中分离出一个低多线性秩张量和一个稀疏异常张量。 在本工作中，我们提出了一种快速的非凸算法，称为鲁棒张量CUR（RTCUR），用于大规模的TRPCA问题。 RTCUR考虑了交替投影的框架，并利用最近发展的张量纤维CUR分解来显著降低计算复杂度。 RTCUR在合成数据集上相对于最先进方法表现出性能优势，并在真实世界应用如彩色视频背景减除中进一步得到验证。 显示英文摘要

摘要： We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their sum. In this work, we propose a fast non-convex algorithm, coined Robust Tensor CUR (RTCUR), for large-scale TRPCA problems. RTCUR considers a framework of alternating projections and utilizes the recently developed tensor Fiber CUR decomposition to dramatically lower the computational complexity. The performance advantage of RTCUR is empirically verified against the state-of-the-arts on the synthetic datasets and is further demonstrated on the real-world application such as color video background subtraction.