Multi-label data with high dimensionality often occurs,which will produce large time and energy overheads when directly used in classification tasks.To solve this problem,a novel algorithm called multi-label dimension...Multi-label data with high dimensionality often occurs,which will produce large time and energy overheads when directly used in classification tasks.To solve this problem,a novel algorithm called multi-label dimensionality reduction via semi-supervised discriminant analysis(MSDA) was proposed.It was expected to derive an objective discriminant function as smooth as possible on the data manifold by multi-label learning and semi-supervised learning.By virtue of the latent imformation,which was provided by the graph weighted matrix of sample attributes and the similarity correlation matrix of partial sample labels,MSDA readily made the separability between different classes achieve maximization and estimated the intrinsic geometric structure in the lower manifold space by employing unlabeled data.Extensive experimental results on several real multi-label datasets show that after dimensionality reduction using MSDA,the average classification accuracy is about 9.71% higher than that of other algorithms,and several evaluation metrices like Hamming-loss are also superior to those of other dimensionality reduction methods.展开更多
针对多工况条件下球磨机关键负荷参数测量面临的复杂性问题,提出基于流形正则化域适应(domain adaptation with manifold regularization,DAMR)湿式球磨机负荷参数软测量的方法。该方法首先采用集成流形约束、最大方差及最大均值差异寻...针对多工况条件下球磨机关键负荷参数测量面临的复杂性问题,提出基于流形正则化域适应(domain adaptation with manifold regularization,DAMR)湿式球磨机负荷参数软测量的方法。该方法首先采用集成流形约束、最大方差及最大均值差异寻找特征变换矩阵,然后,将源建模领域和未建模领域的特征信息投射到公共子空间,最后,在子空间建立模型得到球磨机关键负荷参数的预测值。实验结果表明该方法能以较高的精度实现未知工况下湿式球磨机关键负荷参数的预测,且该方法对于流程工业多工况软测量和过程监控研究有一定的参考价值。展开更多
基金Project(60425310) supported by the National Science Fund for Distinguished Young ScholarsProject(10JJ6094) supported by the Hunan Provincial Natural Foundation of China
文摘Multi-label data with high dimensionality often occurs,which will produce large time and energy overheads when directly used in classification tasks.To solve this problem,a novel algorithm called multi-label dimensionality reduction via semi-supervised discriminant analysis(MSDA) was proposed.It was expected to derive an objective discriminant function as smooth as possible on the data manifold by multi-label learning and semi-supervised learning.By virtue of the latent imformation,which was provided by the graph weighted matrix of sample attributes and the similarity correlation matrix of partial sample labels,MSDA readily made the separability between different classes achieve maximization and estimated the intrinsic geometric structure in the lower manifold space by employing unlabeled data.Extensive experimental results on several real multi-label datasets show that after dimensionality reduction using MSDA,the average classification accuracy is about 9.71% higher than that of other algorithms,and several evaluation metrices like Hamming-loss are also superior to those of other dimensionality reduction methods.
文摘针对多工况条件下球磨机关键负荷参数测量面临的复杂性问题,提出基于流形正则化域适应(domain adaptation with manifold regularization,DAMR)湿式球磨机负荷参数软测量的方法。该方法首先采用集成流形约束、最大方差及最大均值差异寻找特征变换矩阵,然后,将源建模领域和未建模领域的特征信息投射到公共子空间,最后,在子空间建立模型得到球磨机关键负荷参数的预测值。实验结果表明该方法能以较高的精度实现未知工况下湿式球磨机关键负荷参数的预测,且该方法对于流程工业多工况软测量和过程监控研究有一定的参考价值。
