Some observations are far too voluminous in their raw state to be modeled by predictive modeling algorithms directly.
Common examples include image, audio, and textual data, but could just as easily include tabular data with millions of attributes.
Feature extraction is a process of automatically reducing the dimensionality of these types of observations into a much smaller set that can be modelled.
For tabular data, this might include projection methods like Principal Component Analysis and unsupervised clustering methods. For image data, this might include line or edge detection. Depending on the domain, image, video and audio observations lend themselves to many of the same types of DSP methods.
What about generating new features with higher predictive values from the raw data and concatenate them to the raw data?
For example I have data about student wealth, health, family status and I want somehow generate a new feature I can call Social status whic is generated from the raw data and with high predictive value? Is this possible? linear regression can be a good way I need to discover?