OCTen: Online Compression-based Tensor Decomposition

TL;DR

OCTen introduces a novel online tensor decomposition method that leverages compression to efficiently analyze large, dynamic datasets, outperforming existing methods in accuracy and memory usage.

Contribution

This paper presents OCTen, the first compression-based online parallel CP tensor decomposition algorithm, enabling scalable analysis of evolving large-scale tensor data.

Findings

OCTen achieves comparable or better accuracy than state-of-the-art methods.

It reduces memory usage by 40-200%.

Demonstrates scalability on big tensor datasets.

Abstract

Tensor decompositions are powerful tools for large data analytics as they jointly model multiple aspects of data into one framework and enable the discovery of the latent structures and higher-order correlations within the data. One of the most widely studied and used decompositions, especially in data mining and machine learning, is the Canonical Polyadic or CP decomposition. However, today's datasets are not static and these datasets often dynamically growing and changing with time. To operate on such large data, we present OCTen the first ever compression-based online parallel implementation for the CP decomposition. We conduct an extensive empirical analysis of the algorithms in terms of fitness, memory used and CPU time, and in order to demonstrate the compression and scalability of the method, we apply OCTen to big tensor data. Indicatively, OCTen performs on-par or better than state-of-the-art online and online methods in terms of decomposition accuracy and efficiency, while saving up to 40-200 % memory space.