Open AccessThe Value of Manifold Learning Algorithms in Simplifying Complex Datasets for More Efficacious Analysis
Author(s) -
Muhammad Amjad
Publication year - 2020
Publication title -
sciential
Language(s) - English
Resource type - Journals
ISSN - 2562-1483
DOI - 10.15173/sciential.v1i5.2537
Subject(s) - nonlinear dimensionality reduction , dimensionality reduction , curse of dimensionality , manifold (fluid mechanics) , embedding , computer science , filter (signal processing) , key (lock) , feature (linguistics) , space (punctuation) , clustering high dimensional data , manifold alignment , artificial intelligence , algorithm , data mining , pattern recognition (psychology) , cluster analysis , mechanical engineering , linguistics , philosophy , computer security , engineering , computer vision , operating system
Advances in manifold learning have proven to be of great benefit in reducing the dimensionality of large complex datasets. Elements in an intricate dataset will typically belong in high-dimensional space as the number of individual features or independent variables will be extensive. However, these elements can be integrated into a low-dimensional manifold with well-defined parameters. By constructing a low-dimensional manifold and embedding it into high-dimensional feature space, the dataset can be simplified for easier interpretation. In spite of this elemental dimensionality reduction, the dataset’s constituents do not lose any information, but rather filter it with the hopes of elucidating the appropriate knowledge. This paper will explore the importance of this method of data analysis, its applications, and its extensions into topological data analysis.