"To achieve the most accurate results, it is often necessary to have many features. For example, when analysing simple images at the pixel level, there are often over a million features. Even a simple 1 megapixel image (1000 x 1000) produces 1 million features. With many features come a greater computational cost; it is important to reduce the number of features to maintain reasonable computational speed. Simply eliminating features will eliminate valuable information; techniques such as principal component analysis reduce the number of features in a dataset while preserving most of the information."
"To achieve the most accurate results, it is often necessary to have many features. For example, when analysing simple images at the pixel level, there are often over a million features. Even a simple 1 megapixel image (1000 x 1000) produces 1 million features. With many features come a greater computational cost; it is important to reduce the number of features to maintain reasonable computational speed. Simply eliminating features will eliminate valuable information; techniques such as principal component analysis reduce the number of features in a dataset while preserving most of the information."