Editing Distance matrix (section)

=== Distance matrices using Gaussian mixture distance ===

*[https://www.researchgate.net/publication/220723359_Evaluation_of_Distance_Measures_Between_Gaussian_Mixture_Models_of_MFCCs]* Gaussian mixture distance for performing accurate [[nearest neighbor search]] for information retrieval. Under an established Gaussian finite mixture model for the distribution of the data in the database, the Gaussian mixture distance is formulated based on minimizing the [[Kullback-Leibler divergence]] between the distribution of the retrieval data and the data in database. In the comparison of performance of the Gaussian mixture distance with the well-known [[Euclidean distance|Euclidean]] and [[Mahalanobis distance|Mahalanobis]] distances based on a precision performance measurement, experimental results demonstrate that the Gaussian mixture distance function is superior in the others for different types of testing data.

Potential basic algorithms worth noting on the topic of information retrieval is [[Fish School Search]] algorithm an information retrieval that partakes in the act of using distance matrices in order for gathering collective behavior of fish schools. By using a feeding operator to update their weights

Eq. A:

:<math>
x_i(t+1)=x_{i}(t)- step_{vol} rand(0,1)\frac{x_{i}(t) - B(t)}{distance(x_{i}(t),B(t))},
</math>

Eq. B:

:<math>
x_i(t+1)=x_{i}(t)+step_{vol} rand(0,1)\frac{x_{i}(t) - B(t)}{distance(x_{i}(t),B(t))},
</math>

Stepvol defines the size of the maximum volume displacement preformed with the distance matrix, specifically using a [[Euclidean distance]] matrix.