I’d be interested in seeing other matrix factorizations explored as well. Specifically, I would recommend trying nonnegative matrix factorizations: to quote the Wikipedia article:
This non-negativity makes the resulting matrices easier to inspect. Also, in applications such as processing of audio spectrograms or muscular activity, non-negativity is inherent to the data being considered.
The added constraint may help eliminate spurious patterns: for instance, I suspect the positive/negative singular value distinction might be a red herring (based on past projects I’ve worked on).
I’d be interested in seeing other matrix factorizations explored as well. Specifically, I would recommend trying nonnegative matrix factorizations: to quote the Wikipedia article:
The added constraint may help eliminate spurious patterns: for instance, I suspect the positive/negative singular value distinction might be a red herring (based on past projects I’ve worked on).