Stochastic Mean-Shift Clustering
By: Itshak Lapidot, Yann Sepulcre, Tom Trigano
Potential Business Impact:
Groups similar sounds together automatically.
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.
Similar Papers
Local Cluster Cardinality Estimation for Adaptive Mean Shift
Machine Learning (CS)
Finds groups of things even when they're different sizes.
Stochastic Gradients under Nuisances
Machine Learning (Stat)
Teaches computers to learn even with tricky, hidden info.
Cooperative SGD with Dynamic Mixing Matrices
Machine Learning (CS)
Makes computer learning faster and better.