I have difficulty understanding the concept of feature extraction since there are two main ways to describe it.
The first one refers to mapping the raw data into a vector in R^d or the translation of raw data into the inputs required from a machine learning algorithm. For instance, from text, we can tokenize to get vectors, or extract information from raw pixels from images, etc.
The second definition extracts new features from already existing features and refers to the process of transforming o projecting a space composing of many dimensions into a space of fewer dimensions, such as with PCA. Where extracted features are a combination of the original features.
Although both definitions are similar, the first one is more related to a "construction of features" while the other implies transform already existing features. How could I call for instance to the process of using human expertise to know what to measure from objects and construct a set of features?