演化森林哈希：一种无监督的在线哈希学习算法

doi:10.11959/j.issn.1000-0801.2020055

Abstract

Abstract:

Many unsupervised learning to hash algorithm needs to load all data to memory in the training phase,which will occupy a large memory space and cannot be applied to streaming data.An unsupervised online learning to hash algorithm called evolutionary forest hash (EFH) was proposed.In a large-scale data retrieval scenario,the improved evolution tree can be used to learn the spatial topology of the data.A path coding strategy was proposed to map leaf nodes to similarity-preserved binary code.To further improve the querying performance,ensemble learning was combined,and an online evolving forest hashing method was proposed based on the evolving trees.Finally,the feasibility of this method was proved by experiments on two widely used data sets.

Key words: nearest neighbor query, evolving tree, online, hash learning, ensemble learning

CLC Number:

TP311.3

Zhenyu SHOU,Jiangbo QIAN,Yihong DONG,Huahui CHEN. EFH:an online unsupervised hash learning algorithm[J]. Telecommunications Science, 2020, 36(3): 71-82.

Figures/Tables 6

References 33

[1]	ATKINSON P , ORLOWSKA M . Similarity search in high dimensions via hashing[C]// Proceedings of International Conference on Very Large Data Bases. San Francisco:Morgan Kaufmann, 1999: 518-529.
[2]	ANDONI A , INDYK P . Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions[J]. Communications of the ACM, 2008,51(1): 117-122.
[3]	ATHITSOS V , POTAMIAS M , PAPAPETROU P ,et al. Nearest neighbor retrieval using distance-based hashing[C]// Proceedings of International Conference on Data Engineering. Piscataway:IEEE Press, 2008: 327-336.
[4]	ZONG Z . Exploiting Web images for semantic video indexing via robust sample-specific loss[J]. IEEE Transactions on Multimedia, 2014,16(6): 1677-1689.
[5]	WANG J , ZHANG T , SONG J ,et al. A survey on learning to hash[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018,40(4): 769-790.
[6]	DATAR M , IMMORLICA N , INDYK P ,et al. Locality-sensitive hashing scheme based on P-stable distributions[C]// Proceedings of ACM Symposium on Computational Geometry. New York:ACM Press, 2004: 253-262.
[7]	SHRIVASTAVA A , LI P . Asymmetric LSH (ALSH) for sublinear time maximum inner product search[J]. Advances in Neural Information Processing Systems, 2014(3): 2321-2329.
[8]	ANDONI A , INDYK P , LAARHOVEN T ,et al. Practical and optimal LSH for angular distance[C]// Proceedings of Neural Information Processing Systems. Piscataway:IEEE Press, 2015: 1225-1233.
[9]	QIAN J , ZHU Q , CHEN H . Multi-granularity locality-sensitive bloom filter[J]. IEEE Transactions on Computers, 2015,64(12): 3500-3514.
[10]	HUANG Q , FENG J , ZHANG Y ,et al. Query-aware locality-sensitive hashing for approximate nearest neighbor search[J]. Proceedings of the VLDB Endowment, 2015,9(1): 1-12.
[11]	GONG Y , LAZEBNIK S , et al . Iterative quantization:a procrustean approach to learning binary codes[C]// Proceedings of Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2011: 817-824.
[12]	SALAKHUTDINOV R , HINTON G . Semantic hashing[J]. International Journal of Approximate Reasoning, 2009,50(7): 969-978.
[13]	KULIS B , DARRELL T . Learning to hash with binary reconstructive embeddings[C]// Proceedings of Annual Conference on Neural Information Processing Systems. Redhook:Curran Associates, 2009: 1042-1050.
[14]	KONG W , LI W , GUO M ,et al. Manhattan hashing for large-scale image retrieval[C]// Proceedings of International Conference on Research and Development in Information Retrieval. New York:ACM Press, 2012: 45-54.
[15]	SONG J , YANG Y , YANG Y ,et al. Inter-media hashing for large-scale retrieval from heterogeneous data sources[C]// Proceedings of International Conference on Management of Data. New York:ACM Press, 2013: 785-796.
[16]	NOROUZI M , FLEET D . Minimal loss hashing for compact binary codes[C]// Proceedings of International Conference on Machine Learning. Madison:Omni Press, 2008: 353-360.
[17]	LI J , TIAN D , ALREGIB G . Vector quantization in multiresolution mesh compression[J]. IEEE Signal Processing Letters, 2006,13(10): 616-619.
[18]	KOHONEN T . The self-organizing map[J]. Neurocomputing, 1998,21(1): 1-6.
[19]	VESANTO J , ALHONIEMI E . Clustering of the self-organizing map[J]. IEEE Transactions on Neural Networks, 2000,11(3): 586-600.
[20]	PAKKANEN J , IIVARINENJ , OJA E . The evolving tree—analysis and applications[J]. IEEE Transactions on Neural Networks, 2006,17(3): 591-603.
[21]	BLACKMORE J , MIIKKULAINEN R . Visualizing high-dimensional structure with the incremental grid growing neural network[C]// International Conference on Machine Learning. San Francisco:Morgan Kaufmann, 1995: 55-63.
[22]	FRITZKE B . Growing cell structures:self-organizing network for unsupervised and supervised learning[J]. Neural Networks, 1994,7(9): 1441-1460.
[23]	KOIKKALAINEN P , OJA E . Self-organizing hierarchical feature maps[C]// International Joint Conference on Neural Network. Piscataway:IEEE Press, 1990: 279-284.
[24]	LIU Y , CUI J , HUANG Z ,et al. SK-LSH:an afficient index structure for approximate nearest neighbor search[J]. Proceedings of the VLDB Endowment, 2014,7(9): 745-756.
[25]	ZHOU Z H . Machine learning[M]. Beijing: Tsinghua University PressPress, 2016.
[26]	OZA N C , RUSSELL S . Online bagging and boosting[C]// International Conference on Systems. Piscataway:IEEE Press, 2005: 2340-2345.
[27]	HE K , WEN F , SUN J . K-means hashing:an affinity-preserving quantization method for learning binary compact codes[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2013: 2938-2945.
[28]	GONG Y , LAZEBNIK S , GORDO A ,et al. Iterative quantization:a procrustean approach to learning binary codes for large-scale image retrieval[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2013(35): 2916-2929.
[29]	BREIMAN L . Bagging predictors[J]. Machine Learning, 1996,24(2): 123-140.
[30]	SHARMIN N , LU X . Performance characterization and acceleration of in-memory file systems for Hadoop and Spark applications on HPC clusters[C]// Proceedings of IEEE International Conference on Big Data,Big Data. Piscataway:IEEE Press, 2015: 243-252.
[31]	郭佳睿, 魏进武, 张云勇 . 大数据助力运营商提升规模化运营核心力策略[J]. 电信科学, 2018,34(1): 120-125.
	GUO J R , WEI J W , ZHANG Y Y . Strategies for enhancing core capability of large-scale operation for national telecom operators assisted by big data[J]. Telecommunications Science, 2018,34(1): 120-125.
[32]	陈涛, 鲁萌, 陈彦名 . 运营商大数据技术应用研究[J]. 电信科学, 2017,33(1): 130-134.
	CHEN T , LU M , CHEN Y M . Research on operators’ big data technologies and applications[J]. Telecommunications Science, 2017,33(1): 130-134.
[33]	郭佳睿, 魏进武, 张云勇 . 大数据助力运营商提升规模化运营核心力策略[J]. 电信科学, 2018,34(1): 120-125.
	GUO J R , WEI J W , ZHANG Y Y . Strategies for enhancing core capability of large-scale operation for national telecom operators assisted by big data[J]. Telecommunications Science, 2018,34(1): 120-125.

Metrics

Recommended 0

No Suggested Reading articles found!

EFH:an online unsupervised hash learning algorithm

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 33

Related Articles 15

Metrics

Recommended 0

[1]	Haoyu ZHANG, Li YAO, Chaoxu CHEN, Jianyang SHI, Chao SHEN, Nan CHI. Application of BO-BiGRU based post equalizer in high-speed underwater visible light communication system [J]. Telecommunications Science, 2023, 39(5): 11-19.
[2]	Fengxun GONG, Jiankun LI. Characteristics of simplified frequency-domain Volterra series based S-mode receiver [J]. Telecommunications Science, 2022, 38(6): 100-110.
[3]	Chaojie CHEN, Wei XIE, Linwei HE, Qiang TIAN, Runping HE, Zhefei WANG. Anti-collision online early warning method for construction machinery around transmission line [J]. Telecommunications Science, 2022, 38(5): 95-103.
[4]	Wei LIU,Yanli ZHAGN,Jie LIU,Lijuan XING. Application of wireless LoRa in multi-service state grid e-ubiquitous power internet of things [J]. Telecommunications Science, 2020, 36(9): 94-101.
[5]	Jianliang WEI,Hao JI,Tengyan TIAN,Shengli ZHOU. Public opinion model based on super-influencer integrating network community and content [J]. Telecommunications Science, 2020, 36(5): 93-105.
[6]	Yajie LI,Yongli ZHAO,Shoudong LIU,Jie ZHANG. Overview of research on fiber nonlinear equalization algorithm based on artificial intelligence [J]. Telecommunications Science, 2020, 36(3): 61-70.
[7]	Di HAN,Hua XIAO,Yuan ZHONG,Xuegang LAO,Chao HAN. Technical research of a new type of optical fibre with ultra-low-loss and large effective area [J]. Telecommunications Science, 2020, 36(12): 41-48.
[8]	Zhen XING,Hongxiang WANG,Yuefeng JI. Vector signal processing techniques for all-optical networks [J]. Telecommunications Science, 2019, 35(4): 47-61.
[9]	Bin ZHANG,Peng,YUANJie WU,Lingqing SUN,Qiming FU,Yongze ZHANG,Yu LU. An ultraviolet image enhancement algorithm in Curvelet domain based on human visual characteristics [J]. Telecommunications Science, 2019, 35(3): 99-106.
[10]	Mingwen YUAN,Jiangbo QIAN,Yihong DONG,Huahui CHEN. Research and development of hash retrieval technology based on deep learning [J]. Telecommunications Science, 2018, 34(10): 104-115.
[11]	Qingbo WANG. Performance of constant envelope OFDM system with nonlinear power amplifier [J]. Telecommunications Science, 2016, 32(9): 120-124.
[12]	Pubo Huang,Yu Wei. Nonlinear Blind Source Separation Algorithm Using Glowworm Swarm Optimization with Baffle Effect [J]. Telecommunications Science, 2015, 31(9): 97-102.
[13]	Limin Zhang. Efficient Online Dynamic Allocation Algoritbm of Virtual Macbine in Cloud Computing [J]. Telecommunications Science, 2015, 31(4): 106-113.
[14]	Haowen Shu,Zhaotang Su,Xingjun Wang,Zhiping Zhou. Recent Progress of Silicon Photonics for Middle-Infrared Application [J]. Telecommunications Science, 2015, 31(10): 22-35.
[15]	Shigen Shen,Longjun Huang,Keli Hu,Hongjie Li,Risheng Han,Qiying Cao. Differential Game-Based Optimal Control Method for Preventing Malware Propagation in Online Social Network [J]. Telecommunications Science, 2015, 31(10): 66-73.