Missing Value Estimation for Mixed Attribute Data Sets Using Higher Order Kernels
Missing data imputation is a key issue in learning from incomplete data. Various techniques have been developed with great successes on dealing with missing values in data sets with homogeneous attributes (their independent attributes are all either continuous or discrete). The existing system provides a new setting for missing data imputation, i.e., imputing missing data in data sets with heterogeneous attributes (their independent attributes are of different types), referred t o as imputing mixed-attribute data sets. The system provides various estimators to impute the missing data. A mixture-kernel based iterative estimator is advocated to impute mixed-attribute data sets. The proposed system implements the estimators with higher order kernels such as Spherical kernel and Bayesian kernel.