Large-Scale Learning with Fourier Features and Tensor Decompositions
Authors: Frederiek Wesel, Kim Batselier
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate by means of numerical experiments how our low-rank tensor approach obtains the same performance of the corresponding nonparametric model, consistently outperforming random Fourier features.4 Numerical experiments We implemented our Tensor-Kernel Ridge Regression (T-KRR) algorithm in Mathworks MATLAB 2021a (Update 1) [24] and tested it on several regression and classification problems. |
| Researcher Affiliation | Academia | Frederiek Wesel Delft Center for Systems and Control Delft University of Technology f.wesel@tudelft.nl Kim Batselier Delft Center for Systems and Control Delft University of Technology k.batselier@tudelft.nl |
| Pseudocode | No | The paper describes the algorithm in paragraph text but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Our implementation can be freely downloaded from https://github.com/fwesel/T-KRR and allows reproduction of all experiments in this section. |
| Open Datasets | Yes | We consider five UCI [8] datasets in order to compare the performance of our model with RFF and the GPR/KRR baseline.The Airline dataset [14, 16] is a large-scale regression problem originally considered in [14] |
| Dataset Splits | No | For each dataset, we consider 90% of the data for training and the remaining 10% for testing.Training the model is then accomplished with 2/3N datapoints, with the remaining portion reserved for testing. The paper does not explicitly specify a validation split used in their experiments. |
| Hardware Specification | Yes | All experiments were run on a Dell Inc. Latitude 7410 laptop with 16 GB of RAM and an Intel Core i7-10610U CPU running at 1.80 GHz. |
| Software Dependencies | Yes | We implemented our Tensor-Kernel Ridge Regression (T-KRR) algorithm in Mathworks MATLAB 2021a (Update 1) [24] |
| Experiment Setup | Yes | One sweep of our T-KRR algorithm is defined as updating factor matrices in the order 1 D and then back from D 1. All initial factor matrices were initialized with standard normal numbers and normalized by dividing all entries with their Frobenius norm. For all experiments the number of sweeps of T-KRR algorithm are set to 10. |