Minimum Mahalanobis Distance
Overview
The Mahalanobis Distance is a measure is a
distance measure
between a point and a statistical distribution, representing some category of data points. The
Minmum Mahalanobis Distance algorithm classifies a point by the category with the distribution that
has the smallest distance to the point.
The Mahalanobis Distance is implemented in the
Distance API
Mahalanobis Distance
Given a probability distributions with means = {% \vec{\mu} %} and covariance matrix {% S %}, the mahalanobis distance between amy point
{% \vec{x} %} and the distribtution is defined to be
{% d(\vec{x}) = \sqrt{(\vec{x} - \vec{\mu})^T S ^{-1} (\vec{x} - \vec{\mu})} %}
Minimum Mahalanobis Distance
Given several probability distributions, {% P_1,P_2,...,P_n %}, the minimum mahalanobis classifier classifies a point
{% \vec{x} %} with the distribution to which it has a minimum mahalanobis distance.
