Contents
What is feature space representation?
Feature space refers to the n-dimensions where your variables live (not including a target variable, if it is present). The term is used often in ML literature because a task in ML is feature extraction, hence we view all variables as features.
What is feature space in pattern recognition?
In pattern recognition, a feature space (O ? ) 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.
What do we mean by a feature space?
Given some data, a feature space is just the set of all possible values for a chosen set of features from that data. It is always possible to represent feature values and thus a feature space using only numbers, and further to do so in such a way that the feature space can be interpreted as a real space. What do we mean by real space?
How is closeness defined in a feature space?
This description of ‘closeness’ requires that we represent our data in a feature space. Given a set of features for a concept learning problem, we can interpret the feature set as a feature space.
Which is an example of a 2 dimensional feature space?
Below we can see three possible feature spaces of differing quality for a concept learning problem with 2 features: Examples of different 2 dimensional feature spaces for a concept learning problem. The left-most feature space in the above image depicts an idealised feature space for concept learning.
How are feature spaces used in concept learning?
The left-most feature space in the above image depicts an idealised feature space for concept learning. Red data-points belonging to the target concept are separable from the blue data-points which do not. Here, spatial distance corresponds to conceptual distance, which is exactly what we want.