Among some of the most interesting applications of autoencoders is dimensionality reduction [Wang, Y., et al. (2016)]. Given that we live in a time where data storage is easily accessible and affordable, large amounts of data are currently stored everywhere. However, not everything is relevant information. Consider, for example, a database of video recordings of a home security camera that always faces one direction. Chances are that there is a lot of repeated data in every video frame or image and very little of the data gathered will be useful. We would need a strategy to look at what is really important in those images. Images, by their nature, have a lot of redundant information, and there is usually correlation among image regions, which makes autoencoders very useful in compressing the information in images (Petscharnig, S., et al. (2017)).
To demonstrate the applicability of autoencoders in dimensionality reduction for...
