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.
In this paper, the Multi-dimensional Polynomial Transform is used to convert the Multi-dimensional W Transform (MDDWT) into a series of one-dimensional W transform (DWT). Thus, a new polynomial transform algorithms for MDDWT is obtained. The algorithm needs no complex number operations and is simple in structure. The number of multiplications for computing a r-d DWT is only times that of the common used row-column method. The number of additions is also reduced considerablely. Running time of the algorithm on micro-computers is given and is compared with the common used row-column method.
