林学通瓶2015年2月第60卷第5-6期 37 Zhang P,Zhang W.Li W J,et al.Supervised hashing with latent factor models.In:Proceedings of the 37th ACM Conference on Research and Development in Information Retrieval(SIGIR),Queensland,2014,173-182 38 Zhang D.Wang F.SiL.Composite hashing with multiple information sources.In:Proceedings of the 34th ACM Conference on Research and Development in Information Retrieval(SIGIR),Beijing,2011,225-234 39 Kong W,Li W J,Guo M.Manhattan hashing for large-scale image retrieval.In:Proceedings of the 35th ACM Conference on Research and Development in Information Retrieval(SIGIR),Portland,2012,45-54 40 He J,Liu W,Chang S F.Scalable similarity search with optimized kernel hashing.In:Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),Washington,2010,1129-1138 41 Zhen Y,Yeung D Y.A probabilistic model for multimodal hash function learning.In:Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).Beijing.2012,940-948 42 Liu W,Wang J,Ji R,et al.Supervised hashing with kernels.In:Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Providence,2012,2074-2081 43 Shen F,Shen C.Shi Q.et al.Inductive hashing on manifolds.In:Proceedings of the 26th IEEE Conference on Computer Vision and Pat- tern Recognition(CVPR),Portland,2013,1562-1569 44 Zhu X,Huang Z,Shen H T,et al.Linear cross-modal hashing for efficient multimedia search.In:Proceedings of the 21st ACM Multime- dia (MM).Barcelona.2013,143-152 45 Wu F,Yu Z,Yang Y,et al.Sparse multi-modal hashing.IEEE Trans Multimedia,2014,16:427-439 46 Xu H.Wang J,Li Z.et al.Complementary hashing for approximate nearest neighbor search.In:Proceedings of the 13rd IEEE Interna- tional Conference on Computer Vision (ICCV),Barcelona,2011,1631-1638 47 Kan M,Xu D,Shan S,et al.Semi-supervised hashing via kernel hyperplane learning for scalable image search.IEEE Trans Circuits Syst Video Technol,2014,24:704-713 48 Zhou K.Zha H.Leaming binary codes for collaborative filtering.In:Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),Beijing,2012,498-506 49 Sarkar P,Chakrabarti D,Jordan M.Nonparametric link prediction in dynamic networks.In:Proceedings of the 29th International Con- ference on Machine Learning (ICML),Edinburgh,2012 50 Ou M,Cui P,Wang F.et al.Comparing apples to oranges:A scalable solution with heterogeneous hashing.In:Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),Chicago,2013,230-238 51 Zhang Q.Wu Y.Ding Z,et al.Learning hash codes for efficient content reuse detection.In:Proceedings of the 35th ACM Conference on Research and Development in Information Retrieval (SIGIR),Portland,2012,405-414 52 Bellet A.Habrard A,Sebban M.A survey on metric learning for feature vectors and structured data.arXiv:1306.6709,2013. http://arxiv.org/abs/1306.6709 53 Moran S,Lavrenko V.Osborne M.Variable bit quantization for LSH.In:Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL),Sofia,2013,753-758 Learning to hash for big data:Current status and future trends LI WuJun2&ZHOU ZhiHua2 National Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China; Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 210023,China With the rapid development of information technology,explosion of data has occurred in most areas,which means that we have entered the era of big data.Big data has become one of the most important national strategic resources owing to its wide application in a large variety of areas.As a result,research in both academia and industry has focused greatly on big data processing,including storage,management,and analysis.Because the ultimate goal of big data processing is to mine value from big data,in which machine learning plays a key role,big data machine learning (BDML)has become one of the core directions for big data research.By representing the data as binary code,learning to hash (LH)can dramatically reduce the storage and communication cost,thereby improving the efficiency and scalability of BDML systems.Furthermore,LH can also alleviate the curse of dimensionality in BDML systems.Hence,LH has become a hot research topic in machine learning and BDML.This paper gives a brief introduction to LH. big data,machine learning,learning to hash,big data machine learning doi:10.1360/N972014-00841 4902015 年 2 月 第 60 卷 第 5-6 期 490 37 Zhang P, Zhang W, Li W J, et al. Supervised hashing with latent factor models. In: Proceedings of the 37th ACM Conference on Research and Development in Information Retrieval (SIGIR), Queensland, 2014, 173–182 38 Zhang D, Wang F, Si L. Composite hashing with multiple information sources. In: Proceedings of the 34th ACM Conference on Research and Development in Information Retrieval (SIGIR), Beijing, 2011, 225–234 39 Kong W, Li W J, Guo M. Manhattan hashing for large-scale image retrieval. In: Proceedings of the 35th ACM Conference on Research and Development in Information Retrieval (SIGIR), Portland, 2012, 45–54 40 He J, Liu W, Chang S F. Scalable similarity search with optimized kernel hashing. In: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington, 2010, 1129–1138 41 Zhen Y, Yeung D Y. A probabilistic model for multimodal hash function learning. In: Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Beijing, 2012, 940–948 42 Liu W, Wang J, Ji R, et al. Supervised hashing with kernels. In: Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, 2012, 2074–2081 43 Shen F, Shen C, Shi Q, et al. Inductive hashing on manifolds. In: Proceedings of the 26th IEEE Conference on Computer Vision and Pat￾tern Recognition (CVPR), Portland, 2013, 1562–1569 44 Zhu X, Huang Z, Shen H T, et al. Linear cross-modal hashing for efficient multimedia search. In: Proceedings of the 21st ACM Multime￾dia (MM), Barcelona, 2013, 143–152 45 Wu F, Yu Z, Yang Y, et al. Sparse multi-modal hashing. IEEE Trans Multimedia, 2014, 16: 427–439 46 Xu H, Wang J, Li Z, et al. Complementary hashing for approximate nearest neighbor search. In: Proceedings of the 13rd IEEE Interna￾tional Conference on Computer Vision (ICCV), Barcelona, 2011, 1631–1638 47 Kan M, Xu D, Shan S, et al. Semi-supervised hashing via kernel hyperplane learning for scalable image search. IEEE Trans Circuits Syst Video Technol, 2014, 24: 704–713 48 Zhou K, Zha H. Learning binary codes for collaborative filtering. In: Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Beijing, 2012, 498–506 49 Sarkar P, Chakrabarti D, Jordan M. Nonparametric link prediction in dynamic networks. In: Proceedings of the 29th International Con￾ference on Machine Learning (ICML), Edinburgh, 2012 50 Ou M, Cui P, Wang F, et al. Comparing apples to oranges: A scalable solution with heterogeneous hashing. In: Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Chicago, 2013, 230–238 51 Zhang Q, Wu Y, Ding Z, et al. Learning hash codes for efficient content reuse detection. In: Proceedings of the 35th ACM Conference on Research and Development in Information Retrieval (SIGIR), Portland, 2012, 405–414 52 Bellet A, Habrard A, Sebban M. A survey on metric learning for feature vectors and structured data. arXiv:1306.6709, 2013. http://arxiv.org/abs/1306.6709 53 Moran S, Lavrenko V, Osborne M. Variable bit quantization for LSH. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), Sofia, 2013, 753–758 Learning to hash for big data: Current status and future trends LI WuJun1,2 & ZHOU ZhiHua1,2 1 National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China; 2 Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China With the rapid development of information technology, explosion of data has occurred in most areas, which means that we have entered the era of big data. Big data has become one of the most important national strategic resources owing to its wide application in a large variety of areas. As a result, research in both academia and industry has focused greatly on big data processing, including storage, management, and analysis. Because the ultimate goal of big data processing is to mine value from big data, in which machine learning plays a key role, big data machine learning (BDML) has become one of the core directions for big data research. By representing the data as binary code, learning to hash (LH) can dramatically reduce the storage and communication cost, thereby improving the efficiency and scalability of BDML systems. Furthermore, LH can also alleviate the curse of dimensionality in BDML systems. Hence, LH has become a hot research topic in machine learning and BDML. This paper gives a brief introduction to LH. big data, machine learning, learning to hash, big data machine learning doi: 10.1360/N972014-00841