Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression

Understand

In this paper, we consider the problem of learning high-dimensional tensor regression problems with low-rank structure.

• One of the core challenges associated with learning high-dimensional models is computation since the underlying optimization problems are often non-convex.
• While convex relaxations could lead to polynomial-time algorithms they are often slow in practice.
• On the other hand, limited theoretical guarantees exist for non-convex methods.