Robust Semi-Supervised Learning through Label Aggregation
Authors: Yan Yan, Zhongwen Xu, Ivor Tsang, Guodong Long, Yi Yang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five benchmark datasets demonstrate the superior effectiveness, efficiency and robustness of the proposed algorithm. In this section, we demonstrate the robustness and performance of the proposed algorithm by comparing with eight baselines. |
| Researcher Affiliation | Academia | Yan Yan, Zhongwen Xu, Ivor W. Tsang, Guodong Long, Yi Yang Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney, Australia {yan.yan-3, zhongwen.xu}@student.uts.edu.au,{ivor.tsang, guodong.long, yi.yang}@uts.edu.au |
| Pseudocode | Yes | Algorithm 1 RObust Semi-Supervised Ensemble Learning (ROSSEL) |
| Open Source Code | No | The paper does not provide a link or explicit statement about releasing the source code for the described methodology. |
| Open Datasets | Yes | Five UCI datasets including CNAE9, dna, connect4, protein and rcv1 are used in the experiments. |
| Dataset Splits | No | The paper specifies a 'labeled set (5% samples), unlabeled set (75% samples) and test set (20%)' but does not explicitly define a separate validation set for hyperparameter tuning or early stopping, nor does it mention cross-validation for this purpose. |
| Hardware Specification | Yes | All experiments are conducted on a workstation with an Intel(R) CPU (Xeon(R) E5-2687W v2 @ 3.40GHz) and 32 GB memory. |
| Software Dependencies | No | The paper mentions software like LIBLINEAR and LIBSVM, but does not specify their version numbers or any other software dependencies with specific version information. |
| Experiment Setup | Yes | We select from the range of {10 5, 10 3, 10 1, 100, 101, 103, 105} for the parameters to be tuned in all methods. We empirically set the parameter k-nearest neighbour as 5 for the graph-based methods, Lap SVM and Lap RLS. We use Gaussian kernel K(xi, xj) = exp( ||xi xj||2 2σ2 ) to compute the kernel matrix. The kernel parameter σ is fixed as 1, and all feature matrices are normalized before the experiment. When sampling, we bootstrap 50% labeled data into a bootstrap replicate. |