PrincipalProjections Class

public class PrincipalProjections

Public Class PrincipalProjections

public ref class PrincipalProjections

type PrincipalProjections = class end

Inheritance

Object    PrincipalProjections

Derived

Novacta.AnalyticsPrincipalComponents

Multidimensional weighted points and their statistics

Let us consider points in . Their statistical characteristics are usually analyzed by imposing a weighting scheme to them, that is, a relative weight (possibly elementary, i.e. ) is assigned to point , and controls how the point contributes to the overall statistics, provided that the weights sum up to . Such set of weighted points can thus be expressed by means of the pair , referred to as a weighted multidimensional structure where



is the matrix whose -th row is (the transpose of ), and is the weighting scheme expressed as a sequence:

Given a basis of , a structure can be represented by a cloud, say , which can be formally defined as the triplet , where



is the coordinates matrix w.r.t. of the points in , i.e., its -th row, , stands for the coordinates of point .

The basic statistics of can be defined as follows. Its mean point, the vector , whose coordinates are given by , is



Furthermore, the variance of , say , is defined as follows:



where



is the distance induced by basis via its norm



and scalar product



where and



The columns of are referred to as the active variables observed at the points w.r.t. basis . The covariance matrix of such variables can be defined as follows,



where

Notice how the variance of can be characterized in terms of the covariances w.r.t. to ,



with being the trace operator.

Principal projections

A set of weighted points in a vector space can be represented by different clouds, simply by selecting different bases for that space, but the basic statistics of , its mean and variance constants, are the same irrespective of the cloud selected to analyze . On the contrary, the covariances are not coordinate-free, i.e. they depend on the basis chosen to represent the points in . As a consequence, a question raises about the choice of the basis to apply when observing the data and trying to analyze their variability. Is it possible to select a basis that enhances our comprehension of the variance of , better figuring out how it can be explained given the available evidence?

A possible approach is that of seeking a basis such that the variables in the corresponding cloud become uncorrelated, so simplifying the interpretation of . In addition, if is a high-dimensional structure, another useful goal would be that of approximating the cloud in a lower dimensional space, defined so as to minimize the lost of information due to a representation of obtained in a reduced space. Such tasks can be approached by projecting the points in along its principal directions^[1].

When projecting the points along a direction in , the collection of points resulting from the projection defines a new projected structure, whose variance can contribute to the explanation of . The first principal direction of , say , is thus selected so that the variance of its projected structure, say , is maximal. With a first projected structure, there is associated a residual structure, say , whose points have a variance that is the difference between and , i.e. it represents a measure of the variability not yet explained exploiting the first principal direction. If such a measure is not considered sufficiently low, one determines the second principal direction of , say , obtained by maximizing the variance of under the constraint that is orthogonal to . From the second residual structure , one proceeds in the same way to determine a third principal direction, perpendicular to the previous ones, and so on, until the determination of, say, principal directions able to guarantee the required explanation of the variance.

Instantiation

The cloud exploited to initially represent the structure is referred to as the active cloud. Given a Cloud instance representing , a corresponding PrincipalProjections object can be obtained by calling on it method GetPrincipalProjections.

Projecting points in lower dimensional spaces characterizes several statistical multidimensional methods, such as the Principal Components Analysis, the Correspondence, and the Multiple Correspondence Analyses. All these classes provides methods to create the required PrincipalProjections instances, and properties to access them.

Once created, the PrincipalProjections instance enables the access to the initial cloud via its property ActiveCloud.

The identification of the principal directions of is provided by the eigendecomposition of matrix . In fact, let be the matrix whose columns are the normalized eigenvectors of w.r.t. basis , that is satisfies the conditions



and



where is the diagonal matrix whose entries are the corresponding eigenvalues of in decreasing order. Then the principal directions of satisfy



The rows of can be interpreted as the coordinates w.r.t. basis of the basis, say , whose matrix is given by



and the points in admit the following coordinates matrix w.r.t. :



This argument suggests that a principal cloud of can be defined as the cloud .

Approximations in lower dimensional spaces

The dimension of the active cloud is equal to that of the corresponding principal one. However, often not all the principal variables are taken into account: by keeping only some of the first ones, say , a dimensionality reduction can be achieved, while simultaneously preserving - as much as possible - the original variance of the points in .

A PrincipalProjections instance reports information about such variables. First of all, is returned by property NumberOfDirections. Additional insights are exposed as follows.

Variance breakdowns

The covariance matrix of a principal cloud is characterized as follows:



hence the variance of can be factorized as follows:

A finer factorization of is obtained by taking into account the specific contributions of each point in to the overall variance. Remember that the active and principal clouds represent the same weighted structure , hence the distances among cloud points and the corresponding means are also preserved. Thus one has



The quantity



can thus be interpreted as the amount of the the -th principal variable's variance due to the -th point of the cloud, since



Such values can also be exploited to supply aids to the interpretation of the principal cloud. In particular, the relative contribution of the -th point to the variance of the -th principal variable is defined as the quantity



which is the generic entry of the matrix returned by property Contributions, while the quality of representation of the -th point of on the -th principal direction as



where is the angle between the vectors and . You can inspect the squared cosines by getting property RepresentationQualities.

Relationships between active and principal variables

Given an active cloud and a corresponding principal cloud , one can regress the active variables on the first standardized principal variables as follows. Let be the -th column of , i.e., the -th active variable; furthermore, define as the matrix representing the first columns of , and as the sub-matrix of given by deleting its last rows and columns. Since the points are weighted, the regression can be achieved by applying the principle of Weighted Least Squares to define the following optimization problem:



where, being a column vector of ones,



is the matrix of the first standardized principal coordinates. Thus, for ,



It can be noted that



hence one can define the following matrix of regression coefficients:



Matrix can be analyzed by getting property RegressionCoefficients.

The correlations among the -th active variable and the standardized principal variables can thus be obtained as follows:



hence the following matrix of correlations:



which is returned by property Correlations.

Reference

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