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README.md

Large Scale Spectral Clustering with Landmark-Based Representation

The purpose of this code is mainly educational. It is the implementation of unsupervised learning technique called: Large Scale Spectral Clustering with Landmark-Based Representation

Quick example of how to use the code:

# Pkg.add("MNIST")
using MNIST;

Include the code and load the datasets.

include("LSC.jl");
include("Evaluation.jl");

# reading dataset (60k objects)
data, labels = MNIST.traindata(); # using testdata() instead will return smaller (10k objects) dataset
# normalizing it
data  = (data .- mean(data,2)) / std(data .- mean(data,2));

Now, to perform clustering run the following:

LSCMnistResult = LSCClustering(data, 10, 350, :Kmeans, 5, 0.5);
nmiValue = normalizedMutualInformation(getPartitionSet(LSCMnistResult .assignments, 10) , getPartitionSet(labels + 1 ,10), 60000);

Please visit http://int8.io/large-scale-spectral-clustering-with-landmark-based-representation for details (+ to see some experiments)

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