I am looking for a distance metric that could be used instead of cosine/angular distance for high dimensional data. Metric that is limited the same way as cosine/angular distance is would be great.
The problem I have with cosine/angular distance is its ignorance to magnitude.
If given a vector (2,2) and a vector (3,3) or even a vector (100,100), cosine/angular distance says that these vectors are all similar. L2 distance says these are not similar. But L2 is not suited for high dimensional data. Based on this Q
In my case, the vectors, dimension from 500 to 4k, are generated by CNN, and I need to be able to cluster them. While learning the network, I will be using triplet-loss, and when the model "finishes" learning.
I will be also using the same metric for as baseline model (histograms, static features, SURF)