This is why I asked "what field are you learning Linear Algebra?".
Elsewhere, I've discovered that this poster is going into image processing, which is likely "signals and systems" linear algebra.
In signals and systems, your vectors can have infinite dimension, and these infinite-dimension vectors Fourier-transform into other infinite dimension vectors under a new basis.
Any field with more than "3 dimension" vectors / matricies is very difficult to visualize geometrically. Trying to do so is counter-productive to the understanding of the field. This geometric interpretation is really useful in graphics programming / 3d animation however.
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Or perhaps a more concrete example... your "visualize the matrix in X dimensions" advice just doesn't cut it if you're dealing with an 8x8 matrix JPEG DCT coefficient matrix (https://en.wikipedia.org/wiki/JPEG#Discrete_cosine_transform), unless you can imagine 8-diemsional space in your brain.
On the other hand, imagining the 8x8 matrix as 64 linearly-independent "Basis" to your 64-dimension discrete signal is... easier. (Well... for a definition of easier at least). And the transform from time domain into Fourier domain is a transformation in basis that contains the same information.
Elsewhere, I've discovered that this poster is going into image processing, which is likely "signals and systems" linear algebra.
In signals and systems, your vectors can have infinite dimension, and these infinite-dimension vectors Fourier-transform into other infinite dimension vectors under a new basis.
Any field with more than "3 dimension" vectors / matricies is very difficult to visualize geometrically. Trying to do so is counter-productive to the understanding of the field. This geometric interpretation is really useful in graphics programming / 3d animation however.
---------
Or perhaps a more concrete example... your "visualize the matrix in X dimensions" advice just doesn't cut it if you're dealing with an 8x8 matrix JPEG DCT coefficient matrix (https://en.wikipedia.org/wiki/JPEG#Discrete_cosine_transform), unless you can imagine 8-diemsional space in your brain.
On the other hand, imagining the 8x8 matrix as 64 linearly-independent "Basis" to your 64-dimension discrete signal is... easier. (Well... for a definition of easier at least). And the transform from time domain into Fourier domain is a transformation in basis that contains the same information.