Another recent approach is based on feature extraction.

Generally, two possible goals can be achieved by feature extraction:

The created data space is then usually reduced by a following feature extraction (see also dimensionality reduction).

That's called 'feature extraction,' taking info you're familiar with and ignoring the rest.

Now, as you can see, that maps quite well with the sort of hierarchical feature extraction.

To develop methods for automatic feature extraction from remotely sensed data.

What's done is what's called "feature extraction," and that's the key.

You're taking the biometric data, and you are doing a feature extraction.

Transforming the input data into the set of features is called feature extraction.

Many data analysis software packages provide for feature extraction and dimension reduction.