Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: One way to find near-matches in large datasets is to use hash functions. In recent years locality-sensitive hash functions for various metrics have been given; for the Hamming metric projecting onto  k  bits is simple hash function that performs ll. ,  investigate alternatives to projection. For various parameters hash functions given by complete decoding algorithms for error-correcting codes work better, and asymptotically random codes perform better than projection.
['Daniel M. Gordon', 'Victor S. Miller', 'Peter Ostapenko']
I am writing a research paper on a idea Optimal Hash Functions for Approximate Matches on the $n$ -Cube
[{"from": "human", "value": "I am writing a research paper on a idea Optimal Hash Functions for Approximate Matches on the $n$ -Cube"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: One way to find near-matches in large datasets is to use hash functions. In recent years locality-sensitive hash functions for various metrics have been given; for the Hamming metric projecting onto  k  bits is simple hash function that performs ll. ,  investigate alternatives to projection. For various parameters hash functions given by complete decoding algorithms for error-correcting codes work better, and asymptotically random codes perform better than projection."}]