Construction of networks by associating with submanifolds of almost Hermitian manifolds

TL;DR

This paper explores the construction of networks by associating data with submanifolds in almost Hermitian manifolds, contributing to the understanding of manifold-based data representations in machine learning.

Contribution

It introduces a novel approach to network construction using submanifolds of almost Hermitian manifolds, advancing manifold learning techniques.

Findings

Provides a new geometric framework for data representation.

Enhances understanding of non-linear data structures.

Lays groundwork for future manifold learning algorithms.

Abstract

The idea that data lies on a non-linear space has brought up the concept of manifold learning as a part of machine learning.