Computing
Algorithms for Numerical Analysis in High Dimensions
SIAM Journal on Scientific Computing
Regression with the optimised combination technique
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Training a Support Vector Machine in the Primal
Neural Computation
Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem
SIAM Journal on Matrix Analysis and Applications
A comparison of algorithms for fitting the PARAFAC model
Computational Statistics & Data Analysis
Multivariate Regression and Machine Learning with Sums of Separable Functions
SIAM Journal on Scientific Computing
Tensor Decompositions and Applications
SIAM Review
Data Driven Surface Reflectance from Sparse and Irregular Samples
Computer Graphics Forum
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We present a novel approach for classification using a discretised function representation which is independent of the data locations. We construct the classifier as a sum of separable functions, extending the paradigm of separated representations. Such a representation can also be viewed as a low rank tensor product approximation. The central learning 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 show that our method achieves competitive results on several benchmark data sets which gives evidence for the utility of these representations.