New reconstruction and data processing methods for regression and interpolation analysis of multidimensional big data

Understand

The problems of computational data processing involving regression, interpolation, reconstruction and imputation for multidimensional big datasets are becoming more important these days, because of the availability of data and their widely spread usage in business, technological, scientific and other applications.

• The existing methods often have limitations, which either do not allow, or make it difficult to accomplish many data processing tasks.
• The problems usually relate to algorithm accuracy, applicability, performance (computational and algorithmic), demands for computational resources, both in terms of power and memory, and difficulty working with high dimensions.
• Here, we propose a new concept and introduce two methods, which use local area predictors (input data) for finding outcomes.