Pseudo-determinant

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.

Definition

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

${\displaystyle |\mathbf {A} |_{+}=\lim _{\alpha \to 0}{\frac {|\mathbf {A} +\alpha \mathbf {I} |}{\alpha ^{n-\operatorname {rank} (\mathbf {A} )}}}}$

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

Definition of pseudo-determinant using Vahlen matrix

The Vahlen matrix of a conformal transformation, the Möbius transformation (i.e. ${\displaystyle (ax+b)(cx+d)^{-1}}$ for ${\displaystyle a,b,c,d\in {\mathcal {G}}(p,q)}$), is defined as ${\displaystyle [f]={\begin{bmatrix}a&b\\c&d\end{bmatrix}}}$. By the pseudo-determinant of the Vahlen matrix for the conformal transformation, we mean

${\displaystyle \operatorname {pdet} {\begin{bmatrix}a&b\\c&d\end{bmatrix}}=ad^{\dagger }-bc^{\dagger }.}$

If ${\displaystyle \operatorname {pdet} [f]>0}$, the transformation is sense-preserving (rotation) whereas if the ${\displaystyle \operatorname {pdet} [f]<0}$, the transformation is sense-preserving (reflection).

Computation for positive semi-definite case

If ${\displaystyle A}$ is positive semi-definite, then the singular values and eigenvalues of ${\displaystyle A}$ coincide. In this case, if the singular value decomposition (SVD) is available, then ${\displaystyle |\mathbf {A} |_{+}}$ 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 ${\displaystyle \operatorname {rank} (A)=k}$, so that k is the number of non-zero singular values, we may write ${\displaystyle A=PP^{\dagger }}$ where ${\displaystyle P}$ is some n-by-k matrix and the dagger is complex conjugation. The singular values of ${\displaystyle A}$ are the squares of the singular values of ${\displaystyle P}$ and thus we have ${\displaystyle |A|_{+}=\left|P^{\dagger }P\right|}$, where ${\displaystyle \left|P^{\dagger }P\right|}$ is the usual determinant in k dimensions. Further, if ${\displaystyle P}$ is written as the block column ${\displaystyle P=\left({\begin{smallmatrix}C\\D\end{smallmatrix}}\right)}$, then it holds, for any heights of the blocks ${\displaystyle C}$ and ${\displaystyle C}$, that ${\displaystyle |A|_{+}=\left|C^{\dagger }C+D^{\dagger }D\right|}$.

Application in statistics

If a statistical procedure ordinarily compares distributions in terms of the determinants of variance-covariance matrices then, in the case of singular matrices, this comparison can be undertaken by using a combination of the ranks of the matrices and their pseudo-determinants, with the matrix of higher rank being counted as "largest" and the pseudo-determinants only being used if the ranks are equal.[3] Thus pseudo-determinants are sometime presented in the outputs of statistical programs in cases where covariance matrices are singular.[4]

See also

• Matrix determinant
• Moore–Penrose pseudoinverse, which can also be obtained in terms of the non-zero singular values.

References

1. Minka, T.P. (2001). "Inferring a Gaussian Distribution". PDF
2. Florescu, Ionut (2014). Probability and Stochastic Processes. Wiley. p. 529.
3. SAS documentation on "Robust Distance"
4. Bohling, Geoffrey C. (1997) "GSLIB-style programs for discriminant analysis and regionalized classification", Computers & Geosciences, 23 (7), 739–761 doi: 10.1016/S0098-3004(97)00050-2