I tried replacing the distance function in the code with some simpler distance measures:
Gzip distance: ~3 minutes, 78% accuracy
Euclidean distance: ~0.5 seconds, 93% accuracy
Jaccard distance * : ~0.7 seconds, 94% accuracy
Dice dissimilarity * : ~0.8 seconds, 94% accuracy
* after binarising the images
So, as a distance measure for classifying MNIST digits, GZIP has lower accuracy, and is much more computationally demanding than other measures.
I'm not that familiar with how the GZIP algorithm works, it's kind of interesting that it's so much lower. I wonder whether image focused compression algorithms might do better?
(Edit: Btw, I enjoyed the post; it's a creative idea, the writing and code was great, and it's sparked some good discussion. But after having a closer look, I think the baselines above provide some context to the gzip scores.)
I'm not that familiar with how the GZIP algorithm works, it's kind of interesting that it's so much lower. I wonder whether image focused compression algorithms might do better?
(Edit: Btw, I enjoyed the post; it's a creative idea, the writing and code was great, and it's sparked some good discussion. But after having a closer look, I think the baselines above provide some context to the gzip scores.)