Data volume and high dimensions pose an astounding challenge in text-mining tasks. Inherent noise and the computational cost of processing huge amount of datasets make it even more arduous. The science of dimensionality reduction lies in the art of losing out on only a commensurately small numbers of information and still being able to reduce the high dimension space into a manageable proportion.
For classification and clustering techniques to be applied to text data, for different natural language processing activities, we need to reduce the dimensions and noise in the data so that each document can be represented using fewer dimensions, thus significantly reducing the noise that can hinder the performance.
In this chapter, we will learn different dimensionality reduction techniques and their implementations in R:
The curse of dimensionality
Dimensionality reduction
Correspondence analysis
Singular vector decomposition
ISOMAP – moving toward non-linearity
