Anti-sparse coding for approximate nearest neighbor search
This paper proposes a binarization scheme for vectors of high dimension based on the recent concept of anti-sparse coding, and shows its excellent performance for approximate nearest neighbor search. Unlike other binarization schemes, this framework allows, up to a scaling factor, the explicit recon...
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| creator | Jegou, H. Furon, T. Fuchs, J.-J |
| description | This paper proposes a binarization scheme for vectors of high dimension based on the recent concept of anti-sparse coding, and shows its excellent performance for approximate nearest neighbor search. Unlike other binarization schemes, this framework allows, up to a scaling factor, the explicit reconstruction from the binary representation of the original vector. The paper also shows that random projections which are used in Locality Sensitive Hashing algorithms, are significantly outperformed by regular frames for both synthetic and real data if the number of bits exceeds the vector dimensionality, i.e., when high precision is required. |
| doi_str_mv | 10.1109/ICASSP.2012.6288307 |
| format | Conference Proceeding |
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| ispartof | 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, p.2029-2032 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | approximate neighbors search Approximation methods Artificial neural networks Encoding Hamming embedding Indexes Search problems sparse coding spread representations Vectors |
| title | Anti-sparse coding for approximate nearest neighbor search |
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