A feature vector is an n-dimensional vector that contains essential information that describes the characteristics of an object. For example, it can be an object’s numerical features or a list of numbers taken from the output of a neural network layer.
In AI and data science, feature vectors can be used to represent numeric or symbolic characteristics of an object in mathematical terms for seamless analysis.
Let’s break this down. A data set is usually organized into multiple examples where each example will have several features. However, a feature vector won’t have the same feature for numerous examples. Instead, each example will correspond to one feature vector that will contain all the numerical values for that example object.
Feature vectors are often stacked into a design matrix. In this scenario, each row will be a feature vector for one example. Each column will feature all the examples that correspond to that particular feature. This means that it will be like a matrix, but with just one row and multiple columns (or a single column and multiple rows) like [1,2,3,5,6,3,2,0].