Label propagation in the context of this module refers to a set of semisupervised classification algorithms. In the high level, these algorithms work by forming a fully-connected graph between all points given and solving for the steady-state distribution of labels at each point.
These algorithms perform very well in practice. The cost of running can be very expensive, at approximately O(N^3) where N is the number of (labeled and unlabeled) points. The theory (why they perform so well) is motivated by intuitions from random walk algorithms and geometric relationships in the data.
Input matrix can be either data matrix or affinity matrix, both should contain a labels matrix for labeled nodes.
A function which projects a vector into some higher dimensional space. This implementation supprots RBF and KNN kernels. Using the RBF kernel generates a dense matrix of size O(N^2). KNN kernel will generate a sparse matrix of size O(k*N) which will run much faster.
We can set kernel to "knn" or "rbf"
This is a function to generate data for demonstration, just input the number of points is ok.
max iteration times.
Pkg.add("GadFly")
Pkg.clone("https://github.com/memoiry/labelPropagation.jl")
Example shown below uses Gadfly to draw the plot, so an installation of Gadfly is required. (
using labelPropagation
num_unlabel_samples = 800
Mat_Label, labels, Mat_Unlabel = loadCircleData(num_unlabel_samples)
iter = round(linspace(1,70,5))
res = []
for i in iter
unlabel_data_labels = label_propagation(Mat_Label, Mat_Unlabel, labels, kernel_type = "knn", knn_num_neighbors = 10, max_iter = i)
push!(res, unlabel_data_labels)
end
res = reduce(hcat, res)
show_example(Mat_Label, labels, Mat_Unlabel, res) 
It can be seen that as the iteration carries, label propagates between each nodes, which is quite beautiful.
For more detail, you can access the source code,