Abstract
With the increasing popularity of streaming tensor data such as videos and audios, tensor factorization and completion have attracted much attention recently in this area. Existing work usually assume that streaming tensors only grow in
one mode. However, in many real-world scenarios, tensors may grow in multiple modes (or dimensions), i.e., multi-aspect streaming tensors. Standard streaming methods cannot directly handle this
type of data elegantly. Moreover, due to inevitable
system errors, data may be contaminated by outliers, which cause significant deviations from real
data values and make such research particularly
challenging. In this paper, we propose a novel
method for Outlier-Robust Multi-Aspect Streaming
Tensor Completion and Factorization (OR-MSTC),
which is a technique capable of dealing with missing values and outliers in multi-aspect streaming
tensor data. The key idea is to decompose the
tensor structure into an underlying low-rank clean
tensor and a structured-sparse error (outlier) tensor, along with a weighting tensor to mask missing data. We also develop an efficient algorithm
to solve the non-convex and non-smooth optimization problem of OR-MSTC. Experimental results
on various real-world datasets show the superiority
of the proposed method over the baselines and its
robustness against outliers