In the processing of measured data, the number of operations of the algorithm for picking out outlier data in batches is very large. A large number of linear or nonlinear equations based on the parameter model built a...In the processing of measured data, the number of operations of the algorithm for picking out outlier data in batches is very large. A large number of linear or nonlinear equations based on the parameter model built according to the characteristics of measuring equipment and measured object are to be sovied. This paper presents the criterion of picking out outlier data point by point and a parallel algorithm for picking out outlier data in batches, with regard to large-scale linear regression model. The scalability for the parallel algorithm is analyzed, and the results for the algorithm on a group of computers are given. High speed-up is obtained.展开更多
文摘In the processing of measured data, the number of operations of the algorithm for picking out outlier data in batches is very large. A large number of linear or nonlinear equations based on the parameter model built according to the characteristics of measuring equipment and measured object are to be sovied. This paper presents the criterion of picking out outlier data point by point and a parallel algorithm for picking out outlier data in batches, with regard to large-scale linear regression model. The scalability for the parallel algorithm is analyzed, and the results for the algorithm on a group of computers are given. High speed-up is obtained.
