My data set is comprised of two classes, where each data "point" is a sequence of vectors (ranging from 1000 vectors to 40000+ vectors). Is there a simple classifier to use, or would I have to resort to RNNs?
The vectors within sequences of each class aren't mutually exclusive, and there is a lot of redundancy in the sequences between the two classes.
Posted Jan 6, 2020, 4:01 PM UTC
As far as I know, there are no obvious ways to handle classification with a variable number of features. You'll likely have to do some preprocessing to collapse all available information into a uniform shape before classification. Your goal is to maximise information contents while reducing the input shape. How you should approach this heavily depends on your actual data.
There are a couple of possible approaches, depending on what you think of as the main conceptual within-class similarity, ie what do members of a single class have in common.
If they have similar motifs, then you could try shapelets.
If they are similar (as in low mean-square error) after allowing for warping/realignment, then the place to start is dynamic time warping.
If it's something else, I would be delighted to hear more about it and try to recommend an approach.
There is a nice library sktime on github and the work of Keogh and collaborators is the main recommended reading.
We use RNNs but it’s possible to do with infer discrete and pyro. Personally i’d use an LSTM, it’s just easier and the ecosystem is denser.
I'll be looking forward to the answer to this one.
out of my curiosity, what is the problem you're trying to solve?