  In this paper, we examine the problem of missing data in high-dimensional
datasets by taking into consideration the Missing Completely at Random and
Missing at Random mechanisms, as well as theArbitrary missing pattern.
Additionally, this paper employs a methodology based on Deep Learning and Swarm
Intelligence algorithms in order to provide reliable estimates for missing
data. The deep learning technique is used to extract features from the input
data via an unsupervised learning approach by modeling the data distribution
based on the input. This deep learning technique is then used as part of the
objective function for the swarm intelligence technique in order to estimate
the missing data after a supervised fine-tuning phase by minimizing an error
function based on the interrelationship and correlation between features in the
dataset. The investigated methodology in this paper therefore has longer
running times, however, the promising potential outcomes justify the trade-off.
Also, basic knowledge of statistics is presumed.
