To increase the dimensionality of the dataset

To reduce the dimensionality while preserving as much variance as possible

To classify data into predefined categories

To eliminate all noise from data

The number of data points

The total number of features in the original dataset

The Explained Variance Ratio

A random selection process

Removing correlated features

Selecting the first two components only

Ignoring features with low variance

Discarding data randomly

All of the above

The fact that higher-dimensional spaces contain more noise

The increase in computational complexity with higher dimensions

The inability to visualize high-dimensional data