A feature vector is an n-dimensional vector that holds important information about an object’s features. It might be the numerical characteristics of an object or a list of numbers extracted from the output of a neural network layer, for example.
Feature vectors may be used in AI and data science to describe numeric or symbolic properties of an item in mathematical terms for seamless analysis.
Let’s have a look at this in more detail. A data collection is generally divided into numerous instances, each with its own set of characteristics. A feature vector, on the other hand, will not contain the same feature for many samples. Instead, each example will be represented by a single feature vector, including all numerical data for that particular example object.
In many cases, feature vectors are layered into a design matrix. Each row will be a feature vector for one example in this scenario. Each column will contain all of the examples that relate to that feature. This implies it’ll be similar to a matrix, but with only one row and many columns, or just one column and numerous rows.