论文标题
随机在线CP分解
Randomized Online CP Decomposition
论文作者
论文摘要
CandeComp/Parafac（CP）分解已被广泛用于处理多路数据。对于实时或大规模张量，基于随机采样CP分解算法和在线CP分解算法的思想，本文提出了一种新颖的CP分解算法，称为随机在线CP分解（ROCP）。提出的算法可以避免形成完整的Khatri-Rao产品，从而导致很大程度上提高速度并减少记忆使用情况。合成数据和实际数据的实验结果表明，ROCP算法能够应对具有任意数量尺寸的大规模张量的CP分解。此外，ROCP可以大大减少计算时间和内存使用量，尤其是对于大型张量。
CANDECOMP/PARAFAC (CP) decomposition has been widely used to deal with multi-way data. For real-time or large-scale tensors, based on the ideas of randomized-sampling CP decomposition algorithm and online CP decomposition algorithm, a novel CP decomposition algorithm called randomized online CP decomposition (ROCP) is proposed in this paper. The proposed algorithm can avoid forming full Khatri-Rao product, which leads to boost the speed largely and reduce memory usage. The experimental results on synthetic data and real-world data show the ROCP algorithm is able to cope with CP decomposition for large-scale tensors with arbitrary number of dimensions. In addition, ROCP can reduce the computing time and memory usage dramatically, especially for large-scale tensors.
