Several suggestions on the distance metrics of SPORF that may boost the performance on geodesic learning #346
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Current Problem
The USPORF algorithm now uses the similarity matrix as the output, but it is not good enough compared to other unsupervised machine learning methods.
Describe the solution you'd like
To make it a better algorithm for unsupervised clustering and classification, I plan to introduce different distance metrics that may help boost the classification performance.
The planned metrics are: 1) depth of nearest common ancestor and 2) length of shortest path.
The demo
The demo added implements the two new distance metrics on the two geodesic learning simulation in the USPORF paper, and both of the new distance metrics get better results than the similarity matrix on different k values and different Guassian noise dimensions.
Futhure Enhancement
I have trouble finding the infomation of tree structure and decision paths in USPORF package. If available, I believe it will boost the performance even more and if made a built-in function, it will be much convenient to use. Suggestions and instructions are welcome.
Figure showcase
Figure from the USPORF paper for reference