Semi-Supervised Learning through Label Propagation on Geodesics
Please download the codes for Greedy Gradient Max-Cut (GGMC), Gaussian Random Field (GRF),
Local and Global Consistency (LGC) methods at website:
http://www.cs.columbia.edu/~jebara/code.html
Select the "Semi-Supervised Learning Using Greedy Max-Cut CODE"
Uncompress the downloaded file and include it in your path of matlab.
Together with the released codes, one can make preliminary comparisons.
I have to remove dijkstra.mexw64 because it cannot be uploaded to
the matlab exchange system. I replaced dijkstra.mexw64 with dijkstra.cpp
So you can compile it yourself. A really slow implementation using
matlab programming language is also provided, dijkstra.m
However, dijkstra.m is very slow and not recommended.
The codes may take several hours for each demo
Run "Demo_Coil20.m";"Demo_CBCL.m";"Demo_mnist04data.m"
The parameters can be changed.
Cite As
A paper (2024). Semi-Supervised Learning through Label Propagation on Geodesics (https://www.mathworks.com/matlabcentral/fileexchange/55127-semi-supervised-learning-through-label-propagation-on-geodesics), MATLAB Central File Exchange. Retrieved .
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- MATLAB > Mathematics > Graph and Network Algorithms > Construction >
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