Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information

TL;DR

This paper introduces SIITA, a novel framework for dynamic tensor completion that incorporates side information and handles general incremental tensors, with applications in real-world data analysis.

Contribution

The paper proposes SIITA, the first framework to integrate side information into dynamic tensor completion for general incremental tensors, including non-negative constraints.

Findings

SIITA effectively predicts missing data in real-world dynamic tensors.

Incorporating side information improves completion accuracy.

Non-negative constraints enable interpretable latent clusters.

Abstract

Low rank tensor completion is a well studied problem and has applications in various fields. However, in many real world applications the data is dynamic, i.e., new data arrives at different time intervals. As a result, the tensors used to represent the data grow in size. Besides the tensors, in many real world scenarios, side information is also available in the form of matrices which also grow in size with time. The problem of predicting missing values in the dynamically growing tensor is called dynamic tensor completion. Most of the previous work in dynamic tensor completion make an assumption that the tensor grows only in one mode. To the best of our Knowledge, there is no previous work which incorporates side information with dynamic tensor completion. We bridge this gap in this paper by proposing a dynamic tensor completion framework called Side Information infused Incremental Tensor Analysis (SIITA), which incorporates side information and works for general incremental tensors. We also show how non-negative constraints can be incorporated with SIITA, which is essential for mining interpretable latent clusters. We carry out extensive experiments on multiple real world datasets to demonstrate the effectiveness of SIITA in various different settings.