南京大学学报(自然科学版) ›› 2016, Vol. 52 ›› Issue (6): 1084–.

• • 上一篇    下一篇

基于子模函数构建优化商空间链

张燕平*,张 铃,赵 姝,陈 喜,严远亭   

  • 出版日期:2016-11-21 发布日期:2016-11-21
  • 作者简介:安徽大学计算机科学与技术学院,合肥,230601
  • 基金资助:
    基金项目:国家自然科学基金(61673020,61402006),留学回国人员科研启动基金(第49批) 收稿日期:2016-09-28 *通讯联系人,E­mail:zhangyp2@gmail.com

Constructing optimized quotient space chain based on submodular function

Zhang Yanping*,Zhang Ling,Zhao Shu,Chen Xi,Yan Yuanting   

  • Online:2016-11-21 Published:2016-11-21
  • About author: School of Computer Science and Technology,Anhui University,Hefei,230601,China

摘要: 通过商空间链,可得到特定目标求解的逼近方法,由此可完成处理复杂信息,发现隐含知识,揭示事物和事件的内在规律的任务.但随着数据环境的变化,商逼近近似求解开始遇到挑战,由此引发的关键问题就是怎样构建满足求解精度的商空间链,逼近过程中误差界是多少.结合子模函数优化理论来构建商空间链,并对商逼近过程的逼近精度问题展开研究,证明了商空间可保持目标函数的子模性,可利用简单的贪心策略构建最优商空间链,逼近过程中最大误差界≤[1-(1-1/e)-1].

Abstract: Quotient space chain can be used to find an approximation method for a specific problem.It is efficient to reveal the inherent laws of hidden knowledge in dealing with complex problem.However,in this era of big data,quotient space based problem solving has confronted with some new challenges.One of the crucial issues is to construct quotient space chain which satisfies a given precision and to find out the error boundary during the approximation process.In this paper,we first construct the quotient space chain based on submodular function optimization.And then we explore the error boundary during the approximation process.Finally,we prove that quotient space can holds the submodularity of target function,and the optimal quotient space chain can be obtained by using greedy strategy with error less than[1-(1-1/e)-1].

[1] Zhang B,Zhang L.Theory and applications of problem solving.New York:Elsevier Science Inc.,1992,234. [2] 张 铃,张 钹.问题求解理论及应用——商空间粒度计算理论及应用.北京:清华大学出版社,2007,399.(Zhang L,Zhang B.Theory and applications of problem solving - Quotient space theory based granular computing and its applications.Beijing:Tsinghua University Press,2007,399.) [3] Zhang L,Zhang B.The quotient space theory of problem solving.Fundamenta Informaticae,2004,59(2-3):287-298. [4] 张燕平,张 铃,吴 涛.不同粒度世界的描述法——商空间法.计算机学报,2004,27(3):328-333.(Zhang Y P,Zhang L,Wu T.The representation of different granular worlds:A quotient space.Chinese Journal of Computers,2004,27(3):328-333.) [5] Zhao S,Zhang L,Xu X,et al.Hierarchical description of uncertain information.Information Sciences,2014,268:133-146. [6] 赵 姝,苏建忠,刘倩倩等.分层法求解网络最大流的研究.计算机研究与发展,2014,51(8):1845-1853.(Zhao S,Su J Z,Liu Q Q,et al.To solve maximum flow problem of network with a layering method.Journal of Computer Research and Development,2014,51(8):1845-1853.) [7] 郑 诚,张 铃.网络分析中求最大流的商空间方法.计算机学报,2015,38(8):1705-1712.(Zheng C,Zhang L.The computation of maximum flow in network analysis based on quotient space theory.Chinese Journal of Computers,2015,38(8):1705-1712.) [8] Fujishige S.Submodular functions and optimiza­tion.Amsterdam:Elsevier,2005,410. [9] Bach F.Learning with submodular functions:A convex optimization perspective.Boston:Now Publishers Inc.2013,258. [10] Birkhoff G.On the combination of subalgebras.Mathematical Proceedings of the Cambridge Philosophical Society,1933,29(4):441-464. [11] Wolsey L A.An analysis of the greedy algorithm for the submodular set covering problem.Combinatorica,1982,2(4):385-393. [12] Frieze A M.A cost function property for plant location problems.Mathematical Programming,1974,7(1):245-248. [13] Krause A,Guestrin C,Gupta A,et al.Robust sensor placements at informative and communication­efficient locations.ACM Transactions on Sensor Networks(TOSN),2011,7(4):31. [14] Fujishige S.Polymatroidal dependence structure of a set of random variables.Information and Control,1978,39(1):55-72. [15] Singh A,Krause A,Guestrin C,et al.Efficient planning of informative paths for multiple robots.In:The 20th International Joint Conference on Artificial Intelligence(IJCAI 2007).Hyderabad,India:Morgan Koufmann,2007(7):2204-2211. [16] Mei J,Zhao K,Lu B L.On unconstrained quasi­submodular function optimization.In:Proceed­ings of the 29th AAAI Conference on Artificial Intelligence.Austin,TX:AI Access Foundation,2015,1191-1197. [17] Satsangi Y,Whiteson S,Oliehoek F A.Exploiting submodular value functions for faster dynamic sensor selection.In:Proceedings of the 29th AAAI Conference on Artificial Intelligence.Austin,TX:AI Access Foundation,2015,3356-3363. [18] Chen Y,Javdani S,Karbasi A,et al.Submodular surrogates for value of information.In:Proceedings of the 29th AAAI Conference on Artificial Intelligence.Austin,TX:AI Access Foundation,2015,3511-3518. [19] Ene A,Nguyen H L.Random coordinate descent methods for minimizing decomposable submodular functions.In:The 32nd International Conference on Machine Learning.Lille:International Machine Learning Society,2015:787-795. [20] Barbosa R,Ene A,Nguyen H L,et al.The power of randomization:Distributed submodular maximization on massive datasets.In:The 32nd International Conference on Machine Learning.Lille:International Machine Learning Society,2015:1236-1244. [21] Yu J,Blaschko M.Learning submodular losses with the Lovász hinge.In:Proceedings of the 32nd International Conference on Machine Learning.Lille:International Machine Learning Society,2015:1623-1631.
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