Similarity Measures: Hamming, Euclidean, and Squared Euclidean
Explore the use of various distance metrics, including Hamming similarity search, Euclidean distance, and squared Euclidean distance to measure similarity search in vector databases.
Hamming similarity search
Hamming similarity search involves comparing binary vectors to find those with the highest similarity. This is done by calculating the Hamming distance, which counts the number of differing bits between two binary vectors. Vectors with fewer differing bits are considered more similar. Hamming similarity is particularly useful for applications involving binary representations, such as hashing or binary embedding techniques.
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