Pseudo-determinant

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In linear algebra and statistics, the pseudo-determinant[1] is the product of all non-zero eigenvalues of a square matrix. It coincides with the regular determinant when the matrix is non-singular.

The pseudo-determinant of a square n-by-n matrix A may be defined as:

where |A| denotes the usual determinant, I denotes the identity matrix and rank(A) denotes the matrix rank of A.[2]

Definition of pseudo-determinant using Vahlen matrix

The Vahlen matrix of a conformal transformation, the Möbius transformation (i.e. for ), is defined as . By the pseudo-determinant of the Vahlen matrix for the conformal transformation, we mean

If , the transformation is sense-preserving (rotation) whereas if the , the transformation is sense-preserving (reflection).

Computation for positive semi-definite case

If is positive semi-definite, then the singular values and eigenvalues of coincide. In this case, if the singular value decomposition (SVD) is available, then may be computed as the product of the non-zero singular values. If all singular values are zero, then the pseudo-determinant is 1.

Supposing , so that k is the number of non-zero singular values, we may write where is some n-by-k matrix and the dagger is the conjugate transpose. The singular values of are the squares of the singular values of and thus we have , where is the usual determinant in k dimensions. Further, if is written as the block column , then it holds, for any heights of the blocks and , that .

Application in statistics

See also

References

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