Nonlinear Pairwise Layer and Its Training for Kernel Learning
Authors: Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we find that the proposed structure outperforms other state-of-the-art kernel-based algorithms on various benchmark datasets, and thus the effectiveness of the incorporated pairwise layer with its training approach is demonstrated. |
| Researcher Affiliation | Academia | Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University School of Computer Science and Engineering, Nanjing University of Science and Technology Department of Automation, Tsinghua University |
| Pseudocode | Yes | Algorithm 1: Optimization for (15) via ADMM; Algorithm 2: Algorithm for the KNPL model. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | In the experiments, fifteen real-word datasets from UCI Machine Learning Repository (Blake and Merz 1998) are used to evaluate the performance of KNPL with other kernel learning algorithms. |
| Dataset Splits | Yes | In our model, the regularization parameter γ is tuned by 5-fold cross validation. That is, we randomly partition the training data into 5 subsets, one of which is used for validation in turn and the remaining ones for training. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using methods like "SMO algorithm" and "Alternating Direction Method of Multipliers (ADMM)", but it does not list any specific software components with version numbers. |
| Experiment Setup | Yes | In our model, the regularization parameter γ is tuned by 5-fold cross validation... Stopping criteria kmax = 15 and ϵ = 10 4. ... Set the maximum iteration number T = 10. ... where ρ = 1.1 is the parameter that makes β gradually increase in each loop. |