An Accelerated Stochastic Gradient for Canonical Polyadic Decomposition
This work addresses computational efficiency for tensor decomposition in big data applications, but it is incremental as it builds on existing stochastic gradient methods.
The authors tackled the problem of large-scale canonical polyadic decomposition by extending a stochastic gradient method with Nesterov momentum, resulting in a competitive approach compared to state-of-the-art alternatives on synthetic and real-world data.
We consider the problem of structured canonical polyadic decomposition. If the size of the problem is very big, then stochastic gradient approaches are viable alternatives to classical methods, such as Alternating Optimization and All-At-Once optimization. We extend a recent stochastic gradient approach by employing an acceleration step (Nesterov momentum) in each iteration. We compare our approach with state-of-the-art alternatives, using both synthetic and real-world data, and find it to be very competitive.