Schur functions for approximation problems
arXiv:1805.11758h-index: 17
AI Analysis
Provides a new combinatorial perspective for least squares approximation problems, which may benefit applications like curve clustering.
The paper introduces a combinatorial approach using Schur functions for least squares approximation, offering an alternative to algebraic methods. It demonstrates the approach on curve clustering.
In this paper we propose a new approach to least squares approximation problems. This approach is based on partitioning and Schur function. The nature of this approach is combinatorial, while most existing approaches are based on algebra and algebraic geometry. This problem has several practical applications. One of them is curve clustering. We use this application to illustrate the results.