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

Nimishakavi, Madhav and Mishra, Bamdev and Gupta, Manish and Talukdar, Partha (2018) Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information. In: 27th ACM International Conference on Information and Knowledge Management (CIKM), OCT 22-26, 2018, Torino, ITALY, pp. 307-316.

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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.

Item Type:	Conference Paper
Publisher:	ASSOC COMPUTING MACHINERY
Additional Information:	27th ACM International Conference on Information and Knowledge Management (CIKM), Torino, ITALY, OCT 22-26, 2018
Keywords:	Tensor Decomposition; Online Learning; Side Information; Tucker Decomposition; Nonnegative Decomposition
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	07 Feb 2019 08:56
Last Modified:	07 Feb 2019 08:56
URI:	http://eprints.iisc.ac.in/id/eprint/61669

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