Kernel Partial Least Squares for Stationary Data
Authors: Marco Singer, Tatyana Krivobokova, Axel Munk
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | It is shown both theoretically and in simulations that long range dependence results in slower convergence rates. A protein dynamics example shows high predictive power of kernel partial least squares. [...] To validate the theoretical results of the previous sections, we conducted a simulation study. |
| Researcher Affiliation | Academia | Marco Singer EMAIL Institute for Mathematical Stochastics Georg-August-Universit at G ottingen, 37077, Germany; Tatyana Krivobokova EMAIL Institute for Mathematical Stochastics Georg-August-Universit at G ottingen, 37077, Germany; Axel Munk EMAIL Institute for Mathematical Stochastics Georg-August-Universit at G ottingen, 37077, Germany |
| Pseudocode | No | The paper describes algorithms mathematically and refers to them (e.g., KPLS, KCG) but does not provide structured pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper mentions using a "protein dynamics example" and "T4 Lysozyme (T4L)" data for its application. While these are specific types of data, the paper does not provide concrete access information (e.g., a link, DOI, or specific repository) for the dataset used in their experiments. It describes the data source as "molecular dynamics simulations" but no public access is indicated. |
| Dataset Splits | Yes | The first 50% of the data form a training set to calculate the kernel partial least squares estimator and the remaining data are used for testing. |
| Hardware Specification | No | The paper does not contain any specific details about the hardware (e.g., CPU, GPU models) used to run the simulations or experiments. |
| Software Dependencies | No | The paper discusses various algorithms and mathematical frameworks but does not specify any software names with version numbers that were used for implementation (e.g., programming languages, libraries, solvers). |
| Experiment Setup | Yes | The reproducing kernel Hilbert space is chosen to correspond to the Gaussian kernel k(x, y) = exp( l x y 2), x, y Rd, l = 2, for d = 1. [...] The parameter l > 0 is calculated via cross validation on the training set. In our evaluation we obtained l = 10.22. [...] a maximum of 40 iteration steps. [...] The source parameter is taken to be r = 4.5 and we consider the function f(x) = 4.37 1{3L4(x, 4) 2L4(x, 3) + 1.5L4(x, 9)}, x R. [...] The residuals ε(j) 1 , . . . , ε(j) n are generated as independent standard normally distributed random variables and independent of {X(j) t }n t=1 . The response is defined as y(j) t = f(X(j) t )+ η ε(j) t , t = 1, . . . , n, j = 1, . . . , M, with η = 1/16. |