In pattern recognition

Pattern recognition

In machine learning, pattern recognition is the assignment of some sort of output value to a given input value , according to some specific algorithm. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes...

 and machine learning

Machine learning

Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

, a feature vector is an n-dimensional vector of numerical feature

Features (pattern recognition)

In pattern recognition, features are the individual measurable heuristic properties of the phenomena being observed. Choosing discriminating and independent features is key to any pattern recognition algorithm being successful in classification...

s that represent some object. Many algorithm

Algorithm

In mathematics and computer science, an algorithm is an effective method expressed as a finite list of well-defined instructions for calculating a function. Algorithms are used for calculation, data processing, and automated reasoning...

s in machine learning require a numerical representation of objects, since such representations facilitate processing and
statistical analysis. When representing images, the feature values might correspond to the pixels of an image, when representing texts perhaps to term occurrence frequencies.

The vector space

Vector space

A vector space is a mathematical structure formed by a collection of vectors: objects that may be added together and multiplied by numbers, called scalars in this context. Scalars are often taken to be real numbers, but one may also consider vector spaces with scalar multiplication by complex...

 associated with these vectors is often called the feature space

Feature space

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...

. In order to reduce the dimensionality of the feature space, a number of dimensionality reduction

Dimensionality reduction

In machine learning, dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction.-Feature selection:...

 techniques can be employed.

See also

• Feature extraction
  Feature extraction
  In pattern recognition and in image processing, feature extraction is a special form of dimensionality reduction.When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant then the input data will be transformed into a reduced representation...
  
  
• Feature selection
  Feature selection
  In machine learning and statistics, feature selection, also known as variable selection, feature reduction, attribute selection or variable subset selection, is the technique of selecting a subset of relevant features for building robust learning models...
  
  
• Dimensionality reduction
  Dimensionality reduction
  In machine learning, dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction.-Feature selection:...