When the data is too large to be processed, it is transformed into a reduced representation set of features. The process of transforming the input data into a set of features is called feature extraction. Indeed, feature extraction starts from an initial set of measured data and builds derivative values that can retain the information contained in the original dataset but emptied of redundant data.
The extraction of features means simplifying the cost of resources required to describe a large set of data accurately. When performing complex data analysis, one of the biggest problems is to limit the number of variables involved. An analysis of a large number of variables generally requires a large use of memory and processing ...
