离群值
多线性映射
张量(固有定义)
计算机科学
趋同(经济学)
信号处理
算法
人工智能
数学
数字信号处理
计算机硬件
经济增长
经济
纯数学
作者
Le Trung Thanh,Karim Abed-Meraim,Nguyen Linh Trung,Adel Hafiane
标识
DOI:10.1109/tsp.2022.3201640
摘要
Canonical Polyadic (CP) decomposition is a powerful multilinear algebra tool for analyzing multiway (a.k.a. tensor) data and has been used for various signal processing and machine learning applications. When the underlying tensor is derived from data streams, adaptive CP decomposition is required. In this paper, we propose a novel method called robust adaptive CP decomposition (RACP) for dealing with high-order incomplete streaming tensors that are corrupted by outliers. At each time instant, RACP first performs online outlier rejection to accurately detect and remove sparse outliers, and then performs tensor factor tracking to efficiently update the tensor basis. A unified convergence analysis of RACP is also established in that the sequence of generated solutions converges asymptotically to a stationary point of the objective function. Extensive experiments were conducted on both synthetic and real data to demonstrate the effectiveness of RACP in comparison with state-of-the-art adaptive CP algorithms.
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