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Dimensionality Reduction : Overview, Technical Details, and Some Applications

Oskolkov, Nikolay LU (2022) In Tourism on the verge p.151-167
Abstract

Dimensionality reduction is an Exploratory Data Analysis (EDA) approach allowing for fast visualization of high-dimensional data and the possibility of discovering hidden systematic patterns within a data set. While linear dimensionality reduction techniques, such as Principal Component Analysis (PCA), are considered the golden standard in many areas of data science, they seem to be inadequate for analyzing non-linear high-dimensional data (e.g., images, text, gene expression). Instead, in this case, non-linear dimensionality reduction with t-distributed Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP) have been widely used, providing state-of-the-art methods to explore high-dimensional data. This... (More)

Dimensionality reduction is an Exploratory Data Analysis (EDA) approach allowing for fast visualization of high-dimensional data and the possibility of discovering hidden systematic patterns within a data set. While linear dimensionality reduction techniques, such as Principal Component Analysis (PCA), are considered the golden standard in many areas of data science, they seem to be inadequate for analyzing non-linear high-dimensional data (e.g., images, text, gene expression). Instead, in this case, non-linear dimensionality reduction with t-distributed Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP) have been widely used, providing state-of-the-art methods to explore high-dimensional data. This chapter will give an overview of dimension reduction techniques, with a particular focus on PCA, tSNE, and UMAP and their applications within the fields of data science and computational biology.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
High-dimensional data, MDS, PCA, The Curse of Dimensionality, tSNE, UMAP
host publication
Applied data science in tourism : Interdisciplinary approaches, methodologies, and applications - Interdisciplinary approaches, methodologies, and applications
series title
Tourism on the verge
editor
Egger, Roman
pages
17 pages
publisher
Springer Nature
external identifiers
  • scopus:85166070411
ISSN
2366-262X
2366-2611
ISBN
978-3-030-88389-8
978-3-030-88388-1
DOI
10.1007/978-3-030-88389-8_9
language
English
LU publication?
yes
id
be084b68-a5f2-40f1-8cbe-0123bc3dd5b8
date added to LUP
2023-11-21 15:41:53
date last changed
2024-06-14 00:11:58
@inbook{be084b68-a5f2-40f1-8cbe-0123bc3dd5b8,
  abstract     = {{<p>Dimensionality reduction is an Exploratory Data Analysis (EDA) approach allowing for fast visualization of high-dimensional data and the possibility of discovering hidden systematic patterns within a data set. While linear dimensionality reduction techniques, such as Principal Component Analysis (PCA), are considered the golden standard in many areas of data science, they seem to be inadequate for analyzing non-linear high-dimensional data (e.g., images, text, gene expression). Instead, in this case, non-linear dimensionality reduction with t-distributed Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP) have been widely used, providing state-of-the-art methods to explore high-dimensional data. This chapter will give an overview of dimension reduction techniques, with a particular focus on PCA, tSNE, and UMAP and their applications within the fields of data science and computational biology.</p>}},
  author       = {{Oskolkov, Nikolay}},
  booktitle    = {{Applied data science in tourism : Interdisciplinary approaches, methodologies, and applications}},
  editor       = {{Egger, Roman}},
  isbn         = {{978-3-030-88389-8}},
  issn         = {{2366-262X}},
  keywords     = {{High-dimensional data; MDS; PCA; The Curse of Dimensionality; tSNE; UMAP}},
  language     = {{eng}},
  pages        = {{151--167}},
  publisher    = {{Springer Nature}},
  series       = {{Tourism on the verge}},
  title        = {{Dimensionality Reduction : Overview, Technical Details, and Some Applications}},
  url          = {{http://dx.doi.org/10.1007/978-3-030-88389-8_9}},
  doi          = {{10.1007/978-3-030-88389-8_9}},
  year         = {{2022}},
}