We present a novel algorithm called PG-means which is able to learn the number of clusters in a classical Gaussian mixture model. Our method is robust and efficient; it uses statistical hypothesis tests on one-dimensional projections of the data and model to determine if the examples are well represented by the model. In so doing, we are applying a statistical test for the entire model at once, not just on a per-cluster basis. We show that our method works well in difficult cases such as non-Gaussian data, overlapping clusters, eccentric clusters, high dimension, and many true clusters. Further, our new method provides a much more stable estimate of the number of clusters than existing methods.
P G-means algorithm REF is an improvement on the G-means algorithm, where the number of clusters in a Gaussian mixture model is obtained by applying the Kolmogorov-Smirnov (KS) test to the one-dimensional projection of the entire dataset; P G-means also works well when the true clusters are overlapping with each other.
PG-means: learning the number of clusters in data
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 19
Computer Science