Dimensionality Reduction : Overview, Technical Details, and Some Applications
(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.
(Less)
- author
- Oskolkov, Nikolay LU
- organization
- publishing date
- 2022
- 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
- 978-3-030-88388-1
- 978-3-030-88389-8
- 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
- 2024-06-28 01:33:07
@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 = {{978-3-030-88388-1}},
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}},
}