A Bayesian Approach to Unsupervised Feature Selection and Density Estimation Using Expectation Propagation

We propose an approximate Bayesian approach for unsupervised feature selection and density estimation, where the importance of the features for clustering is used as the measure for feature selection. Traditional maximumlikelihood (ML) model-parameter optimization schemes estimate the feature saliencies for a fixed model structure (i.e., a fixed number of clusters). In practice, the number of clusters present in the data for mixture-based modeling is unknown. In an ML framework, the number of clusters typically needs to be ascertained prior to estimating the feature saliencies. We propose a density estimation scheme that addresses model complexity (number of clusters present) and model-parameter estimation (feature saliencies) in a single optimization framework. The approximate Bayesian approach presented here, based on the expectation propagation method, obtains a full posterior distribution on the saliency of the features, along with full posterior distribution of other model parame...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Shaorong Chang, Nilanjan Dasgupta, Lawrence Carin
• Keywords: Approximate Bayesian Approach, Computer Vision, CVPR 2005, Density Estimation Scheme, Feature Saliencies, Fixed Model Structure, Unsupervised Feature Selection