Variable Kernel Density Estimation in High-Dimensional Feature Spaces
Authors: Christiaan van der Walt, Etienne Barnard
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the performance of this estimator to state-of-the art maximumlikelihood estimators on a number of representative high-dimensional machine learning tasks and show that the newly introduced minimum leave-one-out entropy estimator performs optimally on a number of highdimensional datasets considered. |
| Researcher Affiliation | Academia | Christiaan M. van der Walt a,b Etienne Barnard b a Modelling and Digital Science, CSIR, Pretoria, South Africa cvdwalt@csir.co.za b Multilingual Speech Technologies Group, North-West University, Vanderbijlpark, South Africa etienne.barnard@nwu.ac.za |
| Pseudocode | No | The paper contains mathematical derivations and equations but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it contain explicit code release statements or links. |
| Open Datasets | Yes | We select five RW datasets (with independent train and test sets) from the UCI Machine Learning Repository (Lichman 2013) for the purpose of simulation studies. |
| Dataset Splits | Yes | We therefore perform 10-fold cross-validation on each class specific training set to find the optimal number of training iterations for each class conditional density function. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The optimal kernel bandwidth is obtained with a direct approach where the right-hand side of each bandwidth estimation equation is initialised with the Silverman bandwidth, the left hand side is updated, and the updated bandwidth is then substituted into the right-hand side again. This process is repeated for 10 iterations on each training set. |