Dimensionality reduction is an important machine learning technique that reduces the number of features and, at the same time, retains as much information as possible. It is usually performed by obtaining a set of new principal features.
As mentioned before, it is difficult to visualize data of high dimension. Given a three-dimensional plot, we sometimes don't even find it too straightforward to observe any findings, not to mention 10, 100, or 1,000 dimensions. Moreover, some of the features in high dimensional data may be correlated and, as a result, bring in redundancy. This is why we need dimensionality reduction.
Dimensionality reduction is not simply taking out a pair of two features from the original ...
