TY - GEN
T1 - Adaptive quantization for hashing
T2 - 14th SIAM International Conference on Data Mining, SDM 2014
AU - Xiong, Caiming
AU - Chen, Wei
AU - Chen, Gang
AU - Johnson, David
AU - Corso, Jason J.
N1 - Publisher Copyright:
© SIAM.
PY - 2014
Y1 - 2014
N2 - Large-scale data mining and retrieval applications have increasingly turned to compact binary data representations as a way to achieve both fast queries and efficient data storage: many algorithms have been proposed for learning effective binary encodings. Most of these algorithms focus on learning a set of projection hyperplanes for the data and simply binarizing the result from each hyperplane, but this neglects the fact that informativeness may not be uniformly distributed across the projections. In this paper, we address this issue by proposing a novel adaptive quantization (AQ) strategy that adaptively assigns varying numbers of bits to different hyperplanes based on their information content. Our method provides an information-based schema that preserves the neighborhood structure of data points, and we jointly find the globally optimal bit-allocation for all hyperplanes. In our experiments, we compare with state-of-the-art methods on four large-scale datasets and find that our adaptive quantization approach significantly improves on traditional hashing methods.
AB - Large-scale data mining and retrieval applications have increasingly turned to compact binary data representations as a way to achieve both fast queries and efficient data storage: many algorithms have been proposed for learning effective binary encodings. Most of these algorithms focus on learning a set of projection hyperplanes for the data and simply binarizing the result from each hyperplane, but this neglects the fact that informativeness may not be uniformly distributed across the projections. In this paper, we address this issue by proposing a novel adaptive quantization (AQ) strategy that adaptively assigns varying numbers of bits to different hyperplanes based on their information content. Our method provides an information-based schema that preserves the neighborhood structure of data points, and we jointly find the globally optimal bit-allocation for all hyperplanes. In our experiments, we compare with state-of-the-art methods on four large-scale datasets and find that our adaptive quantization approach significantly improves on traditional hashing methods.
UR - http://www.scopus.com/inward/record.url?scp=84959923283&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959923283&partnerID=8YFLogxK
U2 - 10.1137/1.9781611973440.20
DO - 10.1137/1.9781611973440.20
M3 - Conference contribution
AN - SCOPUS:84959923283
T3 - SIAM International Conference on Data Mining 2014, SDM 2014
SP - 172
EP - 180
BT - SIAM International Conference on Data Mining 2014, SDM 2014
A2 - Zaki, Mohammed J.
A2 - Banerjee, Arindam
A2 - Parthasarathy, Srinivasan
A2 - Ning-Tan, Pang
A2 - Obradovic, Zoran
A2 - Kamath, Chandrika
Y2 - 24 April 2014 through 26 April 2014
ER -