Mahalanobis

Return mahalanobis distance of two data matrices A and B (row = object, column = feature)% @author: Kardi Teknomo% http://people.revoledu.com/kardi/index.htmlSee more explanation in the Mahalanobis Distance tutorial http://people.revoledu.com/kardi/tutorial/...isDistance.html Platforms: Matlab

The Mahalanobis distance between a pattern measurement vector of dimensionality D and the center of the class it belongs to is distributed as a chi^2 with D degrees of freedom, when an infinite training set is used. However, the distribution of Mahalanobis distance becomes either Fisher or Beta... Platforms: Matlab

Return covariance matrix for given data matrix X (row = object, column = feature)% @author: Kardi Teknomo% http://people.revoledu.com/kardi/index.htmlSee the usage for Mahalanobis distance computation in http://people.revoledu.com/kardi/tutorial/...isDistance.html Platforms: Matlab

This test is based on the Wilks'method (1963) designed for detection of a single outlier from a normal multivariate sample and approaching the maximun squared Mahalanobis distance to a F distribution function by the Yang and Lee (1987) formulation. A significative squared Mahalanobis distance... Platforms: Matlab

On the literature there are available several tests of the multivariate normality (ca. 50). Among them is the graphic approach based on a chi-square quantile-quantile plot of the observations' squared Mahalanobis distances. Besides the graphic q-q approaching, in this file we proposes an... Platforms: Matlab

The DEMO includes 5 feature selection algorithms:d-deD? Sequential Forward Selection (SFS)d-deD? Sequential Floating Forward Selection (SFFS)d-deD? Sequential Backward Selection (SBS)d-deD? Sequential Floating Backward Selection (SFBS)d-deD? ReliefFTwo CCR estimation methods:d-deD?... Platforms: Matlab

The Expectation-Maximization algorithm (EM) is widely used to find the parameters of a mixture of Gaussian probability density functions (pdfs) or briefly Gaussian components that fits the sample measurement vectors in maximum likelihood sense [1]. In our work, the expectation-maximization (EM)... Platforms: Matlab

Calculates the Mardia's multivariate skewness and kurtosis coefficients as well as their corresponding statistical tests. For large sample size the multivariate skewness is asymptotically distributed as a Chi-square random variable; here it is corrected for small sample size. Likewise, the... Platforms: Matlab

Discriminant Analysis is a multivariate technique concerned with separating distinct sets of observations to previously defined groups; rather exploratory in nature, it is a separatory procedure. Here, we develop this technique considering normal populations with equal covariance matrices. So,... Platforms: Matlab