# A Statistical Model for Tensor Principal Component Analysis

Calvin Lab Auditorium

I will discuss the Principal Component Analysis problem for large tensors of arbitrary order k under a single-spike (or rank-one plus noise) model. On the one hand, using information theory, and recent results in probability theory I will provide necessary and sufficient conditions under which the principal component can be estimated using unbounded computational resources. It turns out that this is possible as soon as the signal-to-noise ratio \beta becomes larger than C\sqrt{k\log k} (and in particular \beta can remain bounded has the problem dimensions increase). On the other hand, I will analyze several polynomial-time estimation algorithms, based on various approaches:

- Tensor matricization and spectral analysis,
- Semidefinite relaxations,
- Power iteration.

I will show that, unless the signal-to-noise ratio diverges in the system dimensions, none of these approaches succeeds. This is possibly related to a fundamental limitation of polynomial estimators for this problem. While complexity theory suggests that intractability holds from a worst case point of view, no analogous result has been proved under statistical models of the data.