In pattern recognition a feature space is an abstract space where each pattern sample is represented as a point in n-dimensional space. Its dimension is determined by the number of features used to describe the patterns. Similar samples are grouped together, which allows the use of density estimation for finding patterns.
The concept is a most used one in classification techniques like k nearest neighbors or support vector machines.
Famous quotes containing the words feature and/or space:
“The proclamation and repetition of first principles is a constant feature of life in our democracy. Active adherence to these principles, however, has always been considered un-American. We recipients of the boon of liberty have always been ready, when faced with discomfort, to discard any and all first principles of liberty, and, further, to indict those who do not freely join with us in happily arrogating those principles.”
—David Mamet (b. 1947)
“If we remembered everything, we should on most occasions be as ill off as if we remembered nothing. It would take us as long to recall a space of time as it took the original time to elapse, and we should never get ahead with our thinking. All recollected times undergo, accordingly, what M. Ribot calls foreshortening; and this foreshortening is due to the omission of an enormous number of facts which filled them.”
—William James (1842–1910)