Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information
This addresses the problem of predicting missing values in dynamically growing tensors with side information for applications like data mining, but it is incremental as it extends prior work by incorporating side information and generalizing to multi-aspect growth.
The paper tackles dynamic tensor completion with side information, proposing SIITA to handle general incremental tensors and non-negative constraints, achieving improved performance on real-world datasets.
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.