Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness
Authors: Kazuo Hara, Ikumi Suzuki, Kei Kobayashi, Kenji Fukumizu, Milos Radovanovic
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using real-world datasets, we demonstrate that the proposed method improves k-NN classification performance and outperforms an existing hub-reduction method. |
| Researcher Affiliation | Academia | National Institute of Genetics Mishima, Shizuoka, Japan; Yamagata University Yonezawa, Yamagata, Japan; The Institute of Statistical Mathematics Tachikawa, Tokyo, Japan; University of Novi Sad Novi Sad, Serbia |
| Pseudocode | No | The paper describes the proposed methods mathematically and in prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using a MATLAB script for mutual proximity, 'We used a MATLAB script norm mp gaussi.m distributed at http://ofai.at/ dominik.schnitzer/mp.', which is an existing method. However, it does not provide concrete access to the source code for the methodology described in this paper developed by the authors. |
| Open Datasets | Yes | We used the two datasets from the Kent Ridge Biomedical Dataset Repository, Leukemia and Lung Cancer,7 as well as the two datasets in the UCI machine learning repository, MFeat and ISOLET.8 |
| Dataset Splits | Yes | We assessed performance according to the accuracy of the prediction using leave-one-out cross-validation. |
| 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 mentions using 'MATLAB script' for a baseline comparison but does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For the additional parameter κ, we select a value from [1, n 1] such that the hubness is maximally reduced, where n is the dataset size. We assessed performance according to the accuracy of the prediction using leave-one-out cross-validation. (for different k {1, 5, 10, 20}) |