The Value of Manifold Learning Algorithms in Simplifying Complex Datasets for More Efficacious Analysis

Authors

  • Muhammad Amjad McMaster University Undergraduate Student

DOI:

https://doi.org/10.15173/sciential.v1i5.2537

Keywords:

Manifold learning, principal component analysis, Isomap

Abstract

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.

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Published

2020-12-04

How to Cite

Amjad, M. (2020). The Value of Manifold Learning Algorithms in Simplifying Complex Datasets for More Efficacious Analysis. Sciential - McMaster Undergraduate Science Journal, 1(5), 13-20. https://doi.org/10.15173/sciential.v1i5.2537

Issue

Section

Academic Literature Review