# Tensor-Based Foundations of Ordinary Least Squares and Neural Network Regression Models

arXiv: https://arxiv.org/abs/2411.12873 · HTML: https://mlsanity.com/paper/2411.12873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 13 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [High-Order Tensor Regression in Sparse Convolutional Neural Networks](https://mlsanity.com/paper/2501.01239/) (2025)

## Abstract (arXiv)

This article introduces a novel approach to the mathematical development of Ordinary Least Squares and Neural Network regression models, diverging from traditional methods in current Machine Learning literature. By leveraging Tensor Analysis and fundamental matrix computations, the theoretical foundations of both models are meticulously detailed and extended to their complete algorithmic forms. The study culminates in the presentation of three algorithms, including a streamlined version of the Backpropagation Algorithm for Neural Networks, illustrating the benefits of this new mathematical approach.