Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  address the issue of search nearest neighbors in binary embedding codes by asymmetric distance computation. Although asymmetric distance computation can obtain a more accurate result than symmetric distance computation, its speed is much slor. Applying multi-index hashing can accelerate asymmetric distance computation. Hover, it takes more memory to store the index tables. Thus, how to select appropriate binary features for indexing under a certain memory constraint is a key issue to improve asymmetric distance computation. ,  propose a novel feature selection method. When only partial index tables can be loaded into memory, the binary features with the lost quantization error can be selected as a representative for indexing. Experimental results show that the proposed method improves the accuracy up to 8.35% and outperforms the linear scan approach and other feature selection methods.
['Chih-Yi Chiu', 'Yu-Cyuan Liou']
I am writing a research paper on a idea A novel feature selection method for nearest neighbor search in binary embedding codes
[{"from": "human", "value": "I am writing a research paper on a idea A novel feature selection method for nearest neighbor search in binary embedding codes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  address the issue of search nearest neighbors in binary embedding codes by asymmetric distance computation. Although asymmetric distance computation can obtain a more accurate result than symmetric distance computation, its speed is much slor. Applying multi-index hashing can accelerate asymmetric distance computation. Hover, it takes more memory to store the index tables. Thus, how to select appropriate binary features for indexing under a certain memory constraint is a key issue to improve asymmetric distance computation. ,  propose a novel feature selection method. When only partial index tables can be loaded into memory, the binary features with the lost quantization error can be selected as a representative for indexing. Experimental results show that the proposed method improves the accuracy up to 8.35% and outperforms the linear scan approach and other feature selection methods."}]