The Kendall and Mallows Kernels for Permutations
Authors: Yunlong Jiao, Jean-Philippe Vert
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate promising results of the underlying kernels on a large benchmark of high-dimensional biomedical data classification problems. We investigate the performance of classifying high-dimensional biomedical data. Table 2 and Figure 2 (Left) summarize the performance of each model across the datasets. |
| Researcher Affiliation | Academia | MINES Paris Tech CBIO, PSL Research University, Institut Curie, INSERM U900, Paris, France |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We investigate the performance of classifying high-dimensional biomedical data... For that purpose, we collected 10 datasets related to human cancer research publicly available online (Li et al., 2003; Schroeder et al., 2011; Shi et al., 2011), as summarized in Table 1. |
| Dataset Splits | Yes | Except for three datasets that are split into training and test sets, in which case we report the performance on the test set, we perform a 5-fold cross-validation repeated 10 times and report the mean performance over the 5 * 10 = 50 splits to evaluate the performance of the different methods. In addition, on each training set, an internal 5-fold cross-validation is performed to tune parameters |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided. |
| Software Dependencies | No | The paper mentions using SVM and KFD as classifiers, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For KFD-based models, we add 10^-3 on the diagonal of the centered and scaled kernel matrix, as suggested by (Mika et al., 1999). The C parameter of SVM-based models optimized over a grid ranging from 10^-2 to 10^3 in log scale, and the number k of TSP in case of feature selection (ranging from 1 to 5000 in log scale). |