Randomized Online CP Decomposition

TL;DR

This paper introduces ROCP, a novel randomized online CP decomposition algorithm that significantly improves speed and reduces memory usage for large-scale multi-way data tensors, enabling real-time processing.

Contribution

The paper presents a new randomized online CP decomposition method that avoids full Khatri-Rao product formation, enhancing efficiency for large-scale tensor data.

Findings

ROCP reduces computation time significantly.

ROCP decreases memory usage for large tensors.

ROCP effectively handles high-dimensional data.

Abstract

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.