Most datasets contain features (such as attributes or variables) that are highly redundant. In order to remove irrelevant and redundant data to reduce the computational cost and avoid overfitting, you can reduce the features into a smaller subsets without a significant loss of information. The mathematical procedure of reducing features is known as dimension reduction. Dimension reduction is the process of converting data with large dimensions in to data will fewer relevant dimensions.
Nowadays, there has been an explosion of dataset sizes due to the continuous collection of data by sensors, cameras, GPS sensors, setup boxes, phones, and so on. With more data with many dimensions, processing has become difficult and some dimensions may not be relevant for all case studies.
Dimension reduction gives the following benefits:
- The reduction of features can increase the...
