Comments (1)
TieSKey
commented on May 18, 2024
Ok, I spoke too soon. With further testing I got lower recalls.
Seems this algorithm is really good when the distance between a point and it's 1st NN is low or confined to only some dimensions. I'll keep testing to narrow it down.
(While I'm using SIFT1M dataset I'm not using the provided NN but computing my own by modifying the train sub set, which is more representative when working with AR)
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Related Issues (13)
- Dynamic index update HOT 3
- float64 queries should throw an error HOT 1
- unable to install on macos after brew install llvm HOT 10
- Comparison with NMSLib (HNSW) HOT 3
- Problem with non-autotuned indices HOT 3
- Unit Tests for Eigen version changes HOT 2
- Cannot load index from file HOT 4
- ImportError: No module named mrpt HOT 3
- Searching for nearest neighbors in parallel HOT 2
- compiling on clang / osx HOT 3
- Python installation failed on windows HOT 2
- Out-of-bounds access with certain parameter combinations HOT 2
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