The Mixture of Multi-kernel Relevance Vector Machines Model

Konstantinos Blekas, Aristidis Likas
2012 2012 IEEE 12th International Conference on Data Mining  
We present a new regression mixture model where each mixture component is a multi-kernel version of the Relevance Vector Machine (RVM). In the proposed model, we exploit the enhanced modeling capability of RVMs due to their embedded sparsity enforcing properties. Moreover, robustness is achieved with respect to the kernel parameters, by employing a weighted multi-kernel scheme. The mixture model is trained using the maximum a posteriori (MAP) approach, where the Expectation Maximization (EM)
more » ... aximization (EM) algorithm is applied offering closed form update equations for the model parameters. An incremental learning methodology is also presented to tackle the parameter initialization problem of the EM algorithm. The efficiency of the proposed mixture model is empirically demonstrated on the time series clustering problem using various artificial and real benchmark datasets and by performing comparisons with other regression mixture models.
doi:10.1109/icdm.2012.34 dblp:conf/icdm/BlekasL12 fatcat:vwxjtzi3qzf5rghfvhosdsidq4