OCTEN: Online Compression-Based Tensor Decomposition

Ekta Gujral, Ravdeep Pasricha, Tianxiong Yang, Evangelos E. Papalexakis

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 PARAFAC decomposition. However, today's datasets are not static and often grow and change over time. To operate on such large dynamic data, we present OCTEN, the first ever compression-based online parallel implementation for the CP/PARAFAC 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 offline methods in terms of decomposition accuracy and efficiency, while achieving memory savings ranging in 40-200%.