Heikki Linnakangas wrote:
> However, the problem is how to represent and store the
> cross-correlation. For fields with low cardinality, like "gender" and
> boolean "breast-cancer-or-not" you can count the prevalence of all the
> different combinations, but that doesn't scale. Another often cited
> example is zip code + street address. There's clearly a strong
> correlation between them, but how do you represent that?
>
> For scalar values we currently store a histogram. I suppose we could
> create a 2D histogram for two columns, but that doesn't actually help
> with the zip code + street address problem.
In my head the neuron for 'principle component analysis' went on while
reading this. Back in college it was used to prepare input data before
feeding it into a neural network. Maybe ideas from PCA could be helpful?
regards,
Yeb Havinga
