Abstract
Tensor decomposition methods are applied to wide variety of application areas such as signal processing, neuroimaging, bioinformatics, and relational data analysis. However, hampered by the non-convexity of these methods, their statistical performance for dealing with noise and missing entries has not been clearly understood. In this paper, we review the algorithm and statistical performance of a convex optimization based tensor decomposition algorithm. We also explain the limitation of the current approach and point to possible future directions.
