There are three major mathematical and statistical software packages to process multivariate data:
§ MATLAB® [1]
§ SAS® [2]
§ SPSS® [3]
These software packages are based on Multivariate General Linear Hypothesis(MGLH) [4]:
§ All dataset variables are linear
§ Additive
§ Relationships models are linear series of weighted terms.
The MGLH is implemented using the following procedures:
§ Multiple Regression
§ Discriminant Function Analysis
§ Canonical Analysis
§ Principle Components Analysis
§ Formal linear algebra methods
We will now discuss these procedures in detail.
y = b1x1 + b2x2 + ... + bnxn + c
In this equation, y is a dependent variable, bi - regression coefficients and xi - independent variables. This equation evaluates y variance proportion at a significant level and xi relative predictive importance. This method evaluates dependent variable based on independent variable values.
This method determines which variables discriminate between two or more groups on covariance matrix of group variances and
co-variances. Then one of the test statistics for eigenvalue analysis, such as Wilks' Lambda, is used. This method is identical to multivariate analysis of variance or MANOVA. For several groups, additional Discriminant functions can be used.
This method uses optimal variables combination for multiple group Discriminant analysis. The first function is the most informative description, the second is second most, and so on. The functions ought to be independent or orthogonal. The canonical correlation analysis is based primarily on canonical roots or eigenvalues.
This method analyzes correlations of variables and interpretes the Discriminant functions' values. This method places heavy emphasis on results interpretation and will not be reviewed here.
This method has been used to estimate the dataset variance in terms of principle components. The method goals are to reduce data dimensionality, define the most informative components and noise filtering. The standard normalization procedure removes noise, stabilizes the data. Regrettably, this method has limited efficiency as data structure identification tool. The PCA defines mutually-orthogonal or uncorrelated projections set. For square and symmetric matrix with ordered eigenvalues, the first principal component direction coincides with 1st eigenvector direction. The second principal component direction coincides with direction of 2nd eigenvector direction. The procedure iterates until satisfactory accuracy has been achieved.
For symmetric matrix, the eigenvalue and eigenvectors can be found by a Householder reduction procedure and QL algorithm. For
non-square or non-symmetric data matrix A, the singular value decomposition U V' of A can be formed. Here matrix V contains the eigenvectors, and the squared diagonal matrix U contains the eigenvalues [5], [6].
These methods use various norms, determinant, trace and condition to evaluate the matrices distance. Nearly all of those criteria can be represented as various functions of eigenvalues [7], [8].
According to Spectral and Hilbert Theorems, the whole sets of eigenvalues & eigenvectors or eigenvalues & eigenfunctions fully describe matrix or operator. Our methods (Theta Criteria) are constructed from whole sets of eigenvalues & eigenvectors or eigenvalues & eigenfunctions. In this scenario, Theta Criteria methods are more optimal for multivariate applications than existing methods. We studied the Theta Criteria in detail and found these methods to be more precise and accurate than existing methods [9], [10].
Let us assume that Spectral Theorem conditions are fulfilled and symmetrical operator / matrix 
can be diagonalized. Also, orthonormalized basis of exists consisting of its eigenvectors.
In addition, each eigenvalue of is real.
Let and 
be symmetrical matrices or operators. Let us construct set of criteria 
, which can converge on 
. Such criteria will reflect the geometrical changes on some the elements of 
, 
… or 
.
Let evaluate 1st differences 
between weighted eigenvectors 
and 
. Their Euclidean norm, or 
criteria 
can serve as closeness criteria between eigenpairs 
and 
(Figure 1.1.) Analogously, 
can be 
criteria between pairs of eigenpairs , and 
, 
. At last, 
be 
criteria on all eigenpairs 
.
We have found that Theta Criteria are norms. These methods are positive, homogeneous, positively defined and satisfy triangle inequality. The Theta Criteria can be transformed to matrix norm and trace differences.
We formulated distinction types hypotheses for positively defined matrices and . Then we evaluated accuracy of Theta Criteria and 
or 
for very close matrices and for ill-defined matrices. Several Theta Criteria were significantly more accurate than or . Further research is required to obtain functional representation between distinction hypotheses types and Theta Criteria optimal type(s).
Existing application for multivariate data set processing, such as MATLAB® [1], SAS® [2] and SPSS® [3] utilize Multiple Regression Procedure, Discriminant Function Analysis, Canonical Analysis and Principle Components Analysis. Those methods are appropriate for initial stage of data analysis when distinction hypotheses about specific application are not formulated or not adequately described.
If distinction hypotheses were established, then formal linear algebra methods or Theta Criteria can be applied for in-depth application analysis.
The formal linear algebra methods are straightforward by utilizing only matrices' eigenvalues. If the application accuracy specifications are moderate, then these methods will be sufficient.Regrettably, formal linear algebra methods have limited accuracy for complex or ill-defined applications.
If, however, the multivariate application is ill-defined or requires high accuracy, then Theta Criteria deserve serious consideration.