Help for EIGENVEC

PURPOSE:
	EIGENVEC will compute the principal component transformation 
matrix for up to 32 input channels.  The covariance matrix, the transformation
matrix of eigen-vectors, and the eigen-values are printed.  This program
should normally be run via the procedure EIGEN.

OPERATION:

The measure of inter-dimensional correlation in the multi-variate
system is usually defined by the covariance matrix of the multi-variate
data.  The linear transformation that diagonalizes the covariance matrix 
can also be applied to the original data and produce a multi-variate 
system with an inter-dimensional correlation of zero, i.e.; completely
uncorrelated multi-variate data.  The linear transformation that
accomplishes this is the matrix of eigen-vectors or characteristic vectors.

A common application of this transformation is to reduce the 
dimensionality of a multi-variate system.  The objective is to summarize
most of the variance, or information content, in a system with a 
lessor number of 'artificial' variates, i.e.; principal components. 
Effectively, by uncorrelating the system, we are compressing most of the
information into a system with lower dimensionality. 

Assume an n-variate system ( n channels of data ).  Let K be the 
n x n covariance matrix of this data and A be the matrix of eigen-vectors
of K.  Associated with each eigen-vector A(j) there is an eigen-value or 
characteristic root, e(j).

	Let A(j) = (a(1j),a(2j),...,a(nj)) be the eigen-vector corresponding 
	to the jth largest eigen-value.

	Let X = (x(1),x(2),...,x(n)) be the n-variate observation 
	( n-dimensional pixel ).

	Then the jth principle component is: v(j) = A(j)  X = a(ij)x(i)

NOTE:  The jth eigen-value is actually the variance in the jth 
principle componemt dimension.  Therefore, the eigen-values are useful 
as a measure the information content that can be expected in the output 
pictures.
WRITTEN BY:  J. D. Addington,   19 Oct. 1979

CURRENT COGNIZANT PROGRAMMER:  Ron Alley
Made portable for UNIX         W. Huey (CRI) 1994
Removed aliases and lets from tst pdf.  FR76949  May 1995


PARAMETERS:


INP

input data sets

OUT

output data sets

SIZE

The standard Vicar size field (sl,ss,nl,ns)

SL

Starting line

SS

Starting sample

NL

Number of lines

NS

Number of samples

LINC

Compute transform from every nth line.

INC

Compute transform from every nth line and nth sample

CORR

Compute eigenvectors from the correlation matrix

DSTRETCH

Output decorrelation stretch matrix, rather than eigenvector matrix

DSCALE

(Used only with DSTRETCH) Adjust the variance equalization scaling factors by the specified values.

EXCLUDE

Pixels with this DN in all bands will be excluded from all computations.

MSS

The number of input bands, when the input is in MSS format.

USE

When input is MSS and not all bands are to be used, use these bands.

MSSO

The number of output components, if the output is to be in MSS format.

OUTPUT

The components to be output. Default is to output the first components, in order.

SAVE

The name for the parameter dataset to hold the eigenvector matrix.

TSAVE

The name for the parameter dataset to hold the transpose of the eigenvector matrix.

AREA

The subareas to be used to compute statistics. Up to 50 regions (SL,SS,NL,NS) may be entered. Default is to use the entire image.

NVAR

FOR NON-IMAGE DATA ONLY The number of dimensions to the data.

DATA

FOR NON-IMAGE DATA ONLY Up to 500 data values for computing statistics.

See Examples:


Cognizant Programmer: