I've read this wonderful explanation of SVD, where the writer mentions that the columns of $V$ are the principal directions (Summary, #1). Is this also true when the data matrix $X$ is complex?
If I'm not mistaken, when $X$ is complex SVD gets us $X=USV^H$, where $(\cdot)^H$ stands for the conjugate transpose. I tried writing a few scripts, and I seem to be getting that the columns of $\bar{V}$ (conjugate of $V$) are in fact the principal directions. Is that wrong?
The reasoning behind this is that I was trying to use the directions as a base to transform the data. In such case, for example like in a Spherical Harmonics transform, one would obtain the coefficients via $w=Y^HX$, where $Y$ contains the directions (base). I manage to fit SVD notation to this notation by assuming that $\bar{V}$ are the directions, but not $V$.