To reduce the dimensionality while preserving as much variance as possible

To classify data into predefined categories

To increase the dimensionality of the dataset

To eliminate all noise from data

The total number of features in the original dataset

The number of data points

A random selection process

The Explained Variance Ratio

Selecting the first two components only

Removing correlated features

Discarding data randomly

Ignoring features with low variance

The inability to visualize high-dimensional data

The increase in computational complexity with higher dimensions

The fact that higher-dimensional spaces contain more noise

All of the above