Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assum...Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance,while local kernel alignment on different sample actually has different contribution to clustering performance.Therefore this assumption could have a negative effective on clustering performance.To solve this issue,we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment,which can learn a proper weight to clustering performance for each local kernel alignment.Specifically,we introduce a new optimization variable-weight-to denote the contribution of each local kernel alignment to clustering performance,and then,weight,kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame.In addition,we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem.Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm.The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms,which illustrates the effectiveness of the proposed algorithm.展开更多
Due to the severe restrictions of natural conditions and ecological environment,high-altitude mountainous areas usually become the " hard bones" in the battle against poverty. Xueshan Township,Luquan Yi and ...Due to the severe restrictions of natural conditions and ecological environment,high-altitude mountainous areas usually become the " hard bones" in the battle against poverty. Xueshan Township,Luquan Yi and Miao Autonomous County of Yunnan Province,located in the alpine valley of Jinsha River,is a major township with wide and deep poverty,and the incidence of poverty is up to 45. 00%. In recent years,Xueshan Township has insisted on the battle against poverty,made effort to develop the Codonopsis pilosula industry,and successfully developed a road to poverty alleviation through C. pilosula industry,and formed a unique industrial poverty alleviation model by the end of 2018,the incidence of poverty dropped to 0. 74%. Based on field survey and interview,this paper analyzes and summarizes the specific practices,main results,practical experience and promotion and application measures of the poverty alleviation model of C. pilosula planting industry in Xueshan Township,in the hope of providing certain reference for the targeted poverty alleviation in similar areas in Yunnan Province and other provinces of China.展开更多
基金This work was supported by the National Key R&D Program of China(No.2018YFB1003203)National Natural Science Foundation of China(Nos.61672528,61773392,61772561)+1 种基金Educational Commission of Hu Nan Province,China(No.14B193)the Key Research&Development Plan of Hunan Province(No.2018NK2012).
文摘Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample.However,we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance,while local kernel alignment on different sample actually has different contribution to clustering performance.Therefore this assumption could have a negative effective on clustering performance.To solve this issue,we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment,which can learn a proper weight to clustering performance for each local kernel alignment.Specifically,we introduce a new optimization variable-weight-to denote the contribution of each local kernel alignment to clustering performance,and then,weight,kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame.In addition,we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem.Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm.The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms,which illustrates the effectiveness of the proposed algorithm.
基金Commissioned Project of Office of Rural Work Leading Group of Kunming Municipal Committee of the Communist Party of China "Study on the Poverty Alleviation Model of Kunming City in the Context of World Poverty Reduction"Construction Project of Party Branch Secretary’s Studio of "Double Leader" Teachers in Colleges and Universities of the Ministry of Education of China
文摘Due to the severe restrictions of natural conditions and ecological environment,high-altitude mountainous areas usually become the " hard bones" in the battle against poverty. Xueshan Township,Luquan Yi and Miao Autonomous County of Yunnan Province,located in the alpine valley of Jinsha River,is a major township with wide and deep poverty,and the incidence of poverty is up to 45. 00%. In recent years,Xueshan Township has insisted on the battle against poverty,made effort to develop the Codonopsis pilosula industry,and successfully developed a road to poverty alleviation through C. pilosula industry,and formed a unique industrial poverty alleviation model by the end of 2018,the incidence of poverty dropped to 0. 74%. Based on field survey and interview,this paper analyzes and summarizes the specific practices,main results,practical experience and promotion and application measures of the poverty alleviation model of C. pilosula planting industry in Xueshan Township,in the hope of providing certain reference for the targeted poverty alleviation in similar areas in Yunnan Province and other provinces of China.
