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code for the paper "Impact of Channel Variation on One-Class Learning for Spoof Detection"
This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the objective here is to make a clear comparison between the sequential and parallel execution of the clustering steps.
A Javascript version of Alexander Schiendorfer's blog post "A worked example of backpropagation".
Implement mini-batch k-means in PySpark distributed framework and test the performance of the algorithm on standard synthetic datasets