Since humans are visual creatures, understanding a high dimensional dataset (even with more than three dimensions) is impossible. Even for a machine (or say, our machine learning algorithm), it's difficult to model the non-linearity from correlated and high-dimensional features. Here, the dimensionality reduction technique is a savior.
Statistically, dimensionality reduction is the process of reducing the number of random variables to find a low-dimensional representation of the data while preserving as much information as possible.
The overall step in PCA can be visualized naively in the following diagram:
PCA and singular-value decomposition (SVD) are the most popular algorithms for dimensionality reduction. Technically, PCA is a statistical technique that's used to emphasize variation and extract the most significant patterns (that is, features...
