From the Simons Institute Workshop on Spectral Algorithms: From Theory to Practice, here is:

A Statistical Model for Tensor Principal Component Analysis

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.

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

**Join the CompressiveSensing subreddit or the Google+ Community and post there !**

Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

## No comments:

Post a Comment