Dimension reducing techniques are becoming
more and more dominant in data science and model
predictions because it is much more efficient and
comfortable working on a small set of data than very
large data. More often than not the reduced lower
dimensional representation seems to contain the same
properties as that of the higher dimensional space.
Additionally, big sets of data prove to be a problem in
terms of computational environment on both memory
and processing power and hence the need for
dimensionality reduction is key.