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Experiement in paper "Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis"
MATLAB
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core_functions
parateter_tune
utils
.gitignore
Exp1_huge_dataset.m
Exp1_large_dataset.m
Exp1_small_dataset.m
Exp2_test_labeled_curve.m
Exp3_test_sample_curve.m
README.md

README.md

Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis

Experiment part in Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis. The paper has been accepted by IJCAI-19.

Usage

The codes are implemented in MATLAB.

Structure

  • ./datasets: All datasets are available in https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/.
  • ./data: Store processed data including kernel matrix and graph Laplacian.
  • ./result: Store final results used in the paper.
  • ./core_functions: Implementation of compared algorithms.
  • ./parameter_tune: Tune parameters.
  • ./utils: Some utils including constructing kernel matrix and graph Laplacian, drawing curves and optimal parameters setting.

Steps

  1. Download data sets into ./datasets
  2. Run Exp1_*.m for experiment 1.
  3. Run Exp2_test_labeled_curve.m for experiment 2.
  4. Run Exp3_test_sample_curve.m for experiment.
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