Stochastic Mean-Shift Clustering
This is an incremental improvement for clustering tasks, potentially benefiting data analysis and applications like speaker clustering.
The authors tackled the problem of improving mean-shift clustering by introducing a stochastic version that uses random data points and partial gradient ascent steps, resulting in outperformance over standard mean-shift in most cases on synthesized 2D Gaussian mixture samples and applied to speaker clustering.
We present a stochastic version of the mean-shift clustering algorithm. In this stochastic version a randomly chosen sequence of data points move according to partial gradient ascent steps of the objective function. Theoretical results illustrating the convergence of the proposed approach, and its relative performances is evaluated on synthesized 2-dimensional samples generated by a Gaussian mixture distribution and compared with state-of-the-art methods. It can be observed that in most cases the stochastic mean-shift clustering outperforms the standard mean-shift. We also illustrate as a practical application the use of the presented method for speaker clustering.