In both cases, some group assignments must be known before carrying
out the Discriminant Analysis. Such group assignments, or labelling,
may be arrived at in any way. Hence Discriminant Analysis can be
employed as a useful complement to Cluster Analysis (in order to judge
the results of the latter) or Principal Components Analysis.
Alternatively, in star-galaxy separation, for instance, using
digitised images, the analyst may define group (stars, galaxies)
membership visually for a conveniently small *training set* or
*design set*.

Methods implemented in this area are Multiple Discriminant Analysis, Fisher's Linear Discriminant Analysis, and K-Nearest Neighbours Discriminant Analysis.

*Multiple Discriminant Analysis*- (MDA) is also termed Discriminant
Factor Analysis and Canonical Discriminant Analysis. It adopts a
similar perspective to PCA: the rows of the data matrix to be examined
constitute points in a multidimensional space, as also do the group
mean vectors. Discriminating axes are determined in this space, in
such a way that optimal separation of the predefined groups is
attained. As with PCA, the problem becomes mathematically the
eigenreduction of a real, symmetric matrix. The eigenvalues represent
the discriminating power of the associated eigenvectors. The
*n*_{Y}groups lie in a space of dimension at most*n*_{Y}- 1. This will be the number of discriminant axes or factors obtainable in the most common practical case when*n*>*m*>*n*_{Y}(where*n*is the number of rows, and*m*the number of columns of the input data matrix). *Linear Discriminant Analysis*- is the 2-group case of MDA.
It optimally separates two groups, using the
*Mahalanobis metric*or*generalized distance*. It also gives the same linear separating decision surface as Bayesian maximum likelihood discrimination in the case of equal class covariance matrices. *K-NNs Discriminant Analysis*- : Non-parametric (distribution-free) methods dispense with the need for assumptions regarding the probability density function. They have become very popular especially in the image processing area. The K-NNs method assigns an object of unknown affiliation to the group to which the majority of its K nearest neighbours belongs.

There is no best discrimination method. A few remarks concerning the advantages and disadvantages of the methods studied are as follows.

- Analytical simplicity or
computational reasons may lead to initial consideration of
linear discriminant analysis or the NN-rule.
- Linear discrimination is the most widely used in practice. Often
the 2-group method is used repeatedly for the analysis of pairs
of multigroup data (yielding
decision
surfaces for
*k*groups). - To estimate the parameters required in quadratic discrimination
more computation and data is required than in the case of linear
discrimination. If there is not a great difference in the group
covariance matrices, then the latter will perform as well as
quadratic discrimination.
- The
*k*-NN rule is simply defined and implemented, especially if there is insufficient data to adequately define sample means and covariance matrices. - MDA is most appropriately used for
*feature selection*. As in the case of PCA, we may want to focus on the variables used in order to investigate the differences between groups; to create synthetic variables which improve the grouping ability of the data; to arrive at a similar objective by discarding irrelevant variables; or to determine the most parsimonious variables for graphical representational purposes.