Using mixtures of t densities to make inferences in the presence of missing data with a small number of multiply imputed data sets.
Sana RashidRobin MitraRussell J. SteelePublished in: Comput. Stat. Data Anal. (2015)
Keyphrases
- missing data
- small number
- data sets
- missing values
- structure from motion
- low rank
- incomplete data
- incomplete data sets
- motion segmentation
- gaussian density
- training data
- matrix factorization
- probability density function
- database
- multiple imputation
- linear combination
- imputation methods
- neural network
- data imputation
- imprecise data
- missing information
- gaussian mixture
- original data
- expectation maximization
- feature selection
- measurement matrix
- training set
- mixture model