Encord Computer Vision Glossary
A feature vector is a numerical representation of a set of features or characteristics of an object or event. In machine learning, feature vectors are used to represent data samples in a format that can be easily processed by algorithms.
An attribute or characteristic of the data sample is represented by each member of a feature vector. For instance, in image recognition, a feature vector may contain information on the color or shape of the image in addition to pixel values at various points in the image. The spectral characteristics of various audio frequencies across time may be included in a feature vector for speech recognition.
How are feature vectors used in computer vision?
Machine learning algorithms frequently use feature vectors as input and utilize them to discover patterns and relationships in the data. Then, new data samples can be classified or predictions can be made using these patterns.
One important aspect of feature vectors is their dimensionality, which refers to the number of elements in the vector. In general, the more dimensions a feature vector has, the more information it contains about the data sample. However, high-dimensional feature vectors can be more difficult for algorithms to process and may require more computational resources.
Feature selection is the process of selecting a subset of features from a larger set to use in a machine learning model. This can be done for a variety of reasons, including reducing the complexity of the model, improving the interpretability of the results, and increasing the accuracy of the model.
There are numerous methods for selecting features, including feature importance ranking, univariate selection, and manual selection. The most suitable approach will rely on the details of the data and the objectives of the machine learning model.
In general, feature vectors are a key component of machine learning because they give computers a way to describe and interpret data in a way that enables them to discover patterns and relationships. Machine learning models can perform better and be easier to understand by carefully choosing and building feature vectors.
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