Oleg Zabluda's blog
Monday, June 18, 2018
 
Interpretable Discovery in Large Image Data Sets
Interpretable Discovery in Large Image Data Sets
"""
Novelty detection:

clustering: look for observations that fall between clusters
Isolation forest: build a RF, then look for examples that split off early from the main data mas
density-based (e.g., local outlier flow)
Use SVD to create a low-dimensional feature space, project your data down to that space and back, and anything you failed to reconstruct is an anomaly that your model didn’t capture
DEMUD: SVD-based + explanations

Especially good for discovering rare classes. "Explanations justify selections."
"""
https://vimeo.com/252188399
https://vimeo.com/252188399

Labels:


- posted by Oleg Zabluda @ 9:29 AM | permalink | | | technorati | bloglines | feedster
| |

Home

Powered by Blogger