Skip to content

superlj666/Approximate-Manifold-Regularization-Scalable-Algorithm-and-Generalization-Analysis

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 

Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis

This repository provides the code used to run the experiments of the paper "Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis". The paper has been published in IJCAI-19. The paper applied Nystom and PCG to LapRLS, borrowing the idea from FALKON. The implementation also use tricks provided in the repository (https://github.com/LCSL/FALKON_paper).

Usage

The codes are implemented in MATLAB.

Structure

  • ./datasets: All datasets are available in Libsvm Data.
  • ./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.

About

Experiement in paper "Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis", published in IJCAI 2019

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages