Improved matrix model of sequence grid partition based on vector space sampling

Abstract

In information technology and data science predictive analysis work, the goal is to infer guess values as accurately as possible. A more densely spaced grid of points should be set as a tool for operating measurement models. It has a wide range of applications in psychology, education and other social science research fields. Although the grid seems to meet the requirements of further improving the accuracy of reasoning and guessing from a certain point of view, it also means complex calculations. When multiple false values fall between another grid point, the traditional Data Oriented Architecture fails. To guarantee the accuracy and efficiency of the calculation, it is necessary to solve the problem of excessive noise in the prediction of samples in meta-learning to solve the uncertainty caused by the noise database data or the assumption of the matrix model. This paper proposes a grid partition movable inference least squares method, that is, the sparse Bayesian least squares method based on the grid minimum matrix. The method is used for solving the problem that the prediction noise of the sample in the meta-learning is too large, to solve the uncertainty brought by the generated noise database data or the array matrix model assumption. According to the Symplectic Bayesian learning of the sequence matrix model, the basic parameters for processing the input sample database data are determined. The sparse Bayesian algorithm solution is used on the grid, combined with the off-grid inference guess of the sequence matrix model and the grid division. We set a coarse grid of points around the grid of false values. The rough point grid division is set, and the grid division around the false value is divided in detail. Then select more appropriate meta-learning parameters to clean up data. In the experiment analysis, we take the small sample study as the scene and carry on the concrete analysis to the experiment. The test proof in the fitting degree of the algorithm adopted in the paper surpasses the Support Vector Machine (SVM) and the k-Nearest Neighbor (KNN) algorithm by 3.5% and 6.4% respectively. The convergence effect surpasses the contrast plan above 10 and has a bigger superiority in the inference factor thrust. It proves that the optimization method in this paper has a strong promotion effect for the application of data forecasting systems in various industries, and has theoretical value as well as practical significance.