4 accepted papers
As a well-known machine learning algorithm, sparse Bayesian learning (SBL) can find sparse representations in linearly probabilistic models by imposing a sparsity-promoting prior on model coefficients. However, classical SBL algorithms lack the essential theoretical guarantees of global convergence.…
Attention has turned in recently to clustering of collections networks where each network is characterized by a sparse inverse covariance matrix. Here we consider a mixture model of sparse Gaussian graphical models and develop an exact EM algorithm that improves considerably on a previous approximat…
While there is now a significant literature on sparse inverse covariance estimation, all that literature, with only a couple of exceptions, has dealt only with univariate (or scalar) networks where each node carries a univariate signal. However in many, perhaps most, applications, each node may carr…
Time-series clustering involves grouping homogeneous time series together based on certain similarity measures. The mixture AR model (MxAR) has already been developed for time series clustering, as has an associated EM algorithm. However, this EM clustering algorithm fails to perform satisfactorily…