本体稀疏向量衰减迭代计算策略
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摘要
在生物学和医学等领域的工程应用中,往往涉及海量数据的处理和计算.在此背景下,稀疏向量学习算法被引入到这些计算中,旨在提取重要的特性信息,减少计算量.随着本体在基因学等领域的广泛应用,发现本体概念数学化后,其对应向量的维度会异常的高,再加上本体图庞大的规模使得计算量大大增加.出于有效解答此类工程计算问题的需要,考虑本体框架下的稀疏向量学习优化算法.用分解本体稀疏向量的方法得到可求导的新优化模型,通过核参数γ递减过程中断点的估计,以及衰减率的调节得到对应的本体稀疏向量迭代求解算法.通过实验验证了新算法可用于本体相似度计算和本体映射的构建.
In biology,medicine and other fields engineering applications,it often involving huge amounts of data processing and computing.In this context,in order to extract important information of features and to reduce the complexity of calculation,the sparse vector learning algorithm is introduced into these calculations.With the wide application of ontology in the fields of genetics,it found that the dimension of the ontology vector will be extremely high after the mathematical treatment,and the size of ontology graph is large.These facts lead the large increase in computing.To answer the question on effective computational problems in such works,sparse vector learning and optimization algorithm is considered in ontology setting.In this paper,new differentiable optimization model is presented by dividing the ontology sparse vector.The ontology sparse vector iterative algorithm is determined in terms of estimating the breaks during the decreasing of kernel parameter γ and adjusting the rate of decay.Experimental verification show that the new algorithm can be used in ontology similarity computation and to build the ontology mapping.
In biology,medicine and other fields engineering applications,it often involving huge amounts of data processing and computing.In this context,in order to extract important information of features and to reduce the complexity of calculation,the sparse vector learning algorithm is introduced into these calculations.With the wide application of ontology in the fields of genetics,it found that the dimension of the ontology vector will be extremely high after the mathematical treatment,and the size of ontology graph is large.These facts lead the large increase in computing.To answer the question on effective computational problems in such works,sparse vector learning and optimization algorithm is considered in ontology setting.In this paper,new differentiable optimization model is presented by dividing the ontology sparse vector.The ontology sparse vector iterative algorithm is determined in terms of estimating the breaks during the decreasing of kernel parameter γ and adjusting the rate of decay.Experimental verification show that the new algorithm can be used in ontology similarity computation and to build the ontology mapping.
引文
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[9] HUANG X,XU T W,GAO W,et al.Ontology similarity measure and ontology mapping via fast ranking method[J].International Journal of Applied Physics and Mathematics,2011,1(1):54-59.
[10] GAO W,LIANG L.Ontology similarity measure by optimizing ndcg measure and application in physics education[J].Future Communication,Computing,Control and Management,2011,142:415-421.
[11] CRASWELL N,HAWKING D.Overview of the TREC 2003 web track[C].Proceedings of the Twelfth Text Retrieval Conference.Gaithersburg,Maryland,NIST Special Publication,2003:78-92.
[12] 兰美辉,任友俊,徐坚,等.k-部排序本体相似度计算[J].计算机应用,2012,32(4):1 094-1 096.
[13] GAO W,GAO Y,LIANG L.Diffusion and harmonic analysis on hypergraph and application in ontology similarity measure and ontology mapping[J].Journal of Chemical and Pharmaceutical Research,2013,5(9):592-598.
[2] 兰美辉,徐坚,高炜.基于优先图的本体相似度计算[J].科学技术与工程,2014,14(28):252-255.
[3] 兰美辉,徐坚,高炜.基于primal RankRLS方法的本体映射算法[J].云南师范大学学报:自然科学版,2014,34(3):37-40.
[4] LAN M H,XU J,GAO W.Ontology feature extraction via vector learning algorithm and applied to similarity measuring and ontology mapping [J].IAENG International Journal of Computer Science,2016,43(1):10-19.
[5] GAO W,ZHU L L,GUO Y.Multi-dividing infinite push ontology algorithm[J].Engineering Letters,2015,23(3):132-139.
[6] ZHU L L,TAO W G,MIN X Z,et al.Theoretical characteristics of ontology learning algorithm in multi-dividing setting [J].IAENG International Journal of Computer Science,2016,43(2):184-191.
[7] GAO W,ZHU L L,WANG K Y.Ontology sparse vector learning algorithm for ontology similarity measuring and ontology mapping via adal technology[J].International Journal of Bifurcation and Chaos,2015,25(14):1 540 032.
[8] WANG Y,GAO W,ZHANG Y G,et al.Ontology similarity computation use ranking learning method[C].The 3rd International Conference on Computational Intelligence and Industrial Application,Wuhan,China,December 4-5,2010.
[9] HUANG X,XU T W,GAO W,et al.Ontology similarity measure and ontology mapping via fast ranking method[J].International Journal of Applied Physics and Mathematics,2011,1(1):54-59.
[10] GAO W,LIANG L.Ontology similarity measure by optimizing ndcg measure and application in physics education[J].Future Communication,Computing,Control and Management,2011,142:415-421.
[11] CRASWELL N,HAWKING D.Overview of the TREC 2003 web track[C].Proceedings of the Twelfth Text Retrieval Conference.Gaithersburg,Maryland,NIST Special Publication,2003:78-92.
[12] 兰美辉,任友俊,徐坚,等.k-部排序本体相似度计算[J].计算机应用,2012,32(4):1 094-1 096.
[13] GAO W,GAO Y,LIANG L.Diffusion and harmonic analysis on hypergraph and application in ontology similarity measure and ontology mapping[J].Journal of Chemical and Pharmaceutical Research,2013,5(9):592-598.