What does the term Feature Extraction refer to? Deriving a feature from an analogue input like an image Making a selection of a subset of features from a featureset Creating a new set of features by transformation of a featureset

Feature Extraction refers to the process of reducing the amount of resources required to describe a large set of data accurately. This is done by creating a set of combined variables, known as "features".

In the context of your question, it can refer to:

1. Deriving a feature from an analogue input like an image: This involves processing an image to identify and isolate certain unique characteristics or patterns. These could be edges, corners, or other unique aspects that can be used to categorize or identify the image.

2. Making a selection of a subset of features from a featureset: This involves selecting the most relevant features from a larger set. The selected features are those that most effectively achieve the task at hand. This process is also known as feature selection.

3. Creating a new set of features by transformation of a featureset: This involves creating new features from existing ones using various mathematical transformations. This is often done to make the data more suitable for a specific machine learning algorithm. This process is also known as feature engineering.