After much research and review of linear algebra and matrices, I've managed to
work out the basics of this whole Singular Value Decomposition. As with most of
these things, some parts were more difficult, some simpler in retrospect.

So here's what I have so far:

Top left: a unit sphere with a number of randomly placed points on its surface.

Top right: The sphere and points scaled and rotated to give an off-axis
ellipsoid and it's corresponding data points [these will be acquired by trace()
in future version(s) ]

3. The ellipsoid after attempting to axis-align it using a matrix derived from
the SVD.

4. Step 3 rotated 90 deg.

Looks like the new BB is almost 1/3 of the original (which looks horrible to
begin with).

The side view looks a bit tilted, so something's still off, so I will keep
experimenting - hopefully I can streamline the process so I don't have to keep
manually entering values.

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