What is the geometrical significance of a complex-valued singular value decomposition?

Suppose you have a 2x2 real-valued matrix, $$\mathbf{A}$$. If you perform a singular value decomposition (SVD), then this can be understood geometrically as a decomposition of $$\mathbf{A}$$ into a 2-D rotation, scaling and second 2-D rotation of the form:

$$\mathbf{A} = \mathbf{R_1 S R_2}$$

However, if the entries of $$\mathbf{A}$$ are complex, then what is the geometric meaning of this? Now $$\mathbf{R_1}$$ and $$\mathbf{R_2}$$ will also be complex. Are these still rotation matrices? Is there an additional underlying rotation happening in the complex plane? What does it mean if you have a "complex rotation matrix"?

Any help, clarification or references is appreciated.

• They'll be unitary matrices, which can still be thought of as rotations in a suitable sense. – Qiaochu Yuan Nov 15 '18 at 21:27
• Rotation through what angle though? What does it mean to have a "complex rotation matrix"? The matrix multiplication does not preserve real and imaginary components as separate. For example, if $\mathbf{R_1}$ is a complex rotation matrix and you take some real valued vector, $\mathbf{v}$, then $\mathbf{R_1v}$ will be complex valued as well. I'm just struggling to understand what this actually means geometrically. – Darcy Nov 15 '18 at 21:59
• The eigenvalues of a unitary matrix, like an orthogonal matrix, are unit complex numbers $e^{i \theta}$, so can be thought of in terms of rotations by $\theta$. – Qiaochu Yuan Nov 15 '18 at 22:02