A least-squares approximation of partial differential equations with high-dimensional random inputs
Journal of Computational Physics
Digital predistortion for power amplifiers using separable functions
IEEE Transactions on Signal Processing
Classification with sums of separable functions
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Fast high-dimensional approximation with sparse occupancy trees
Journal of Computational and Applied Mathematics
On the computational benefit of tensor separation for high-dimensional discrete convolutions
Multidimensional Systems and Signal Processing
A stochastic conjugate gradient method for the approximation of functions
Journal of Computational and Applied Mathematics
Data Driven Surface Reflectance from Sparse and Irregular Samples
Computer Graphics Forum
Learning to Predict Physical Properties using Sums of Separable Functions
SIAM Journal on Scientific Computing
Journal of Computational Physics
| Hi-index | 0.02 |
We present an algorithm for learning (or estimating) a function of many variables from scattered data. The function is approximated by a sum of separable functions, following the paradigm of separated representations. The central fitting algorithm is linear in both the number of data points and the number of variables and, thus, is suitable for large data sets in high dimensions. We present numerical evidence for the utility of these representations. In particular, we show that our method outperforms other methods on several benchmark data sets.