What is Chi distance?
Chi-square distance is one of the distance measures that can be used as a measure of dissimilarity between two histograms and has been widely used in various applications such as image retrieval, texture and object classification, and shape classification [9].
What is chi-square distance?
Chi-square distance calculation is a statistical method, generally measures similarity between 2 feature matrices. Such distance is generally used in many applications like similar image retrieval, image texture, feature extractions etc. Below given 2 different methods for calculating Chi-square Distance.
How do you read a chi-square formula?
To calculate chi square, we take the square of the difference between the observed (o) and expected (e) values and divide it by the expected value. Depending on the number of categories of data, we may end up with two or more values. Chi square is the sum of those values.
Which is the best definition of Hellinger distance?
Hellinger distance is a metric to measure the difference between two probability distributions. It is the probabilistic analog of Euclidean distance. Given two probability distributions, P and Q, Hellinger distance is defined as: It is useful when quantifying the difference between two probability distributions.
When to use Hellinger distance in row profile?
The Hellinger distance is defined between vectors having only positive or zero elements. In general, it is used for row profiles.
How to calculate the squared Hellinger distance in calculus?
If we denote the densities as f and g, respectively, the squared Hellinger distance can be expressed as a standard calculus integral where the second form can be obtained by expanding the square and using the fact that the integral of a probability density over its domain equals 1.
How to calculate the Hellinger distance between two Gaussian distributions?
The Hellinger distance (or affinity) between two Gaussian distributions can be computed explicitly, just like the square Wasserstein distance and the Kullback-Leibler divergence or relative entropy. Namely equal to 1 1 iff (m1,σ1) =(m2,σ2) ( m 1, σ 1) = ( m 2, σ 2) .