Dimensionality reduction (mixing the rational and irrational)

[rodrigo.constanzo]

@weefuzzy

It’s a case of try-it-and-see. … It’s not at all uncommon to do PCA first and then follow with a non-linear reduction when trying to go from lots of input dimensions to very few.

I’ll try PCA and see how that fares. I think if I’m just in millisecond land, it would (intuitively) give results that made sense that way. If I include derivatives that would probably crumble without sanitization.

Curious about this PCA->non-linear workflow too. By this do you mean do PCA on something to bring it down to a smaller amount of dimensions and then to do MDS on that lower dimensional version?

@weefuzzy

@rodrigo.constanzo

I wanted to keep a sense of the difference in scales between the (currently random) entries,

This is the bit I’m not following. Keep a sense of the difference in scales for the purposes of weighting the dimensions’ relative importance in the reduction?

but I guess that would still be the case if I either standardized/normalized them anyways(?).

No: the normalisaing / standardising is done per dimension, explicitly to remove the difference in ranges. At this point it’s more productive to think about how the input data are distributed

I’m not really clear on things either, but say I have one file with a duration of 5000 and a time centroid of 2500, and another file with a duration of 400 and a time centroid of 200. By not sanitizing the data, I was hoping to maintain that difference in scale, rather than them having somewhat similar values (?) after sanitization.

More practically, I want the fact that the first sample has bigger values to be present in the ‘timeness’ metric that I can query later on, if, for example, I want to choose samples that are ‘timeness’-ier (yikes!).

@weefuzzy

They’re not so different!

In spirit I guess, but I’m still struggling with my normal use cases where I can find the nearest match for certain fields, and then some other criteria for other fields (ala biasing).