Statistical Optimality and Computational Efficiency of Nystrom Kernel PCA
Authors: Nicholas Sterge, Bharath K. Sriperumbudur
JMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we theoretically study the trade-off between computational complexity and statistical accuracy in Nyström approximate kernel principal component analysis (KPCA), wherein we show that the Nyström approximate KPCA matches the statistical performance of (non-approximate) KPCA while remaining computationally beneficial. |
| Researcher Affiliation | Academia | Nicholas Sterge EMAIL Department of Statistics Pennsylvania State University University Park, PA 16802, USA. Bharath K. Sriperumbudur EMAIL Department of Statistics Pennsylvania State University University Park, PA 16802, USA. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. It presents mathematical derivations, propositions, theorems, and proofs. |
| Open Source Code | No | The paper does not contain any explicit statements about providing open-source code for the methodology described, nor does it provide any links to code repositories. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets. It refers to a 'data-generating distribution P' and samples 'Xi i.i.d. P' but does not mention any publicly available or open datasets with access information. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with specific datasets, therefore, there is no mention of training/test/validation dataset splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup that would require specific software dependencies with version numbers. Therefore, no software dependencies are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not contain details about an experimental setup, such as hyperparameters or system-level training settings. |