为了进行连续马尔可夫模型的初值提取,提出一种各类在训练样本空间近似均衡分布的K均值聚类法。在聚类的过程中引入惩罚因子,从而限制过多的训练矢量集中于一个或几个类,使样本空间划分近似均匀。连续马尔可夫模型初值提取实验证明,该...为了进行连续马尔可夫模型的初值提取,提出一种各类在训练样本空间近似均衡分布的K均值聚类法。在聚类的过程中引入惩罚因子,从而限制过多的训练矢量集中于一个或几个类,使样本空间划分近似均匀。连续马尔可夫模型初值提取实验证明,该方法与标准的K均值聚类法、LBG(L inde Buzo G ray)聚类法相比,降低了矢量量化产生的全局失真,各个类在样本空间的分布更加均匀,提高了矢量量化的性能。将该方法用于孤立词识别连续马尔可夫模型的初值提取,可使各个高斯概率密度函数的参数估计更逼近其无偏估计,从而提高了马尔可夫模型初值的可靠性。展开更多
For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the i...For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.展开更多
The K-multiple-means(KMM)retains the simple and efficient advantages of the K-means algorithm by setting multiple subclasses,and improves its effect on non-convex data sets.And aiming at the problem that it cannot be ...The K-multiple-means(KMM)retains the simple and efficient advantages of the K-means algorithm by setting multiple subclasses,and improves its effect on non-convex data sets.And aiming at the problem that it cannot be applied to the Internet on a multi-view data set,a multi-view K-multiple-means(MKMM)clustering method is proposed in this paper.The new algorithm introduces view weight parameter,reserves the design of setting multiple subclasses,makes the number of clusters as constraint and obtains clusters by solving optimization problem.The new algorithm is compared with some popular multi-view clustering algorithms.The effectiveness of the new algorithm is proved through the analysis of the experimental results.展开更多
文摘为了进行连续马尔可夫模型的初值提取,提出一种各类在训练样本空间近似均衡分布的K均值聚类法。在聚类的过程中引入惩罚因子,从而限制过多的训练矢量集中于一个或几个类,使样本空间划分近似均匀。连续马尔可夫模型初值提取实验证明,该方法与标准的K均值聚类法、LBG(L inde Buzo G ray)聚类法相比,降低了矢量量化产生的全局失真,各个类在样本空间的分布更加均匀,提高了矢量量化的性能。将该方法用于孤立词识别连续马尔可夫模型的初值提取,可使各个高斯概率密度函数的参数估计更逼近其无偏估计,从而提高了马尔可夫模型初值的可靠性。
基金National Natural Science Foundation of China(No.51467008)。
文摘For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.
基金National Youth Natural Science Foundationof China(No.61806006)Innovation Program for Graduate of Jiangsu Province(No.KYLX160-781)Project Supported by Jiangsu University Superior Discipline Construction Project。
文摘The K-multiple-means(KMM)retains the simple and efficient advantages of the K-means algorithm by setting multiple subclasses,and improves its effect on non-convex data sets.And aiming at the problem that it cannot be applied to the Internet on a multi-view data set,a multi-view K-multiple-means(MKMM)clustering method is proposed in this paper.The new algorithm introduces view weight parameter,reserves the design of setting multiple subclasses,makes the number of clusters as constraint and obtains clusters by solving optimization problem.The new algorithm is compared with some popular multi-view clustering algorithms.The effectiveness of the new algorithm is proved through the analysis of the experimental results.
