I have a question that I’m having trouble formulating clearly to my colleagues so I though I would try it here.
I’m using a single layer of HTM to perform anomaly detection. My problem is that I have 44+ features and encoding all of them into a single SP leads to bad results (as expected and mentioned here).
So my idea was to try some Feature selection/extraction/Weighting techniques to reduce the amount of features to a reasonable amount.
But the thing is that all these techniques won’t be able to provide any “formal” result but more like a statistical optimum.
Thus, I was wandering if there was a way to know how the proximal connections are connected to the SDR, meaning : Is there a way to know to which feature a particular mini-column is connected ?
The idea would be to find which feature caused, or was mostly responsible for the anomaly to begin with.
I don’t want to deduce which values were responsible for the anomalies but only which features.
Does that make sense ?