Robust Dimensionality Reduction: A Resistant Search for the Relevant Information in Complex Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00565535" target="_blank" >RIV/67985807:_____/23:00565535 - isvavai.cz</a>
Result on the web
<a href="https://dx.doi.org/10.4018/978-1-6684-5092-5.ch004" target="_blank" >https://dx.doi.org/10.4018/978-1-6684-5092-5.ch004</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4018/978-1-6684-5264-6.ch009" target="_blank" >10.4018/978-1-6684-5264-6.ch009</a>
Alternative languages
Result language
angličtina
Original language name
Robust Dimensionality Reduction: A Resistant Search for the Relevant Information in Complex Data
Original language description
With the increasing availability of massive data in various fields of applications such as engineering, economics, or biomedicine, there appears an urgent need for new reliable tools for obtaining relevant knowledge from such data, which allow one to find and interpret the most relevant features (variables). Such interpretation is however infeasible for the habitually used methods of machine learning, which can be characterized as black boxes. This chapter is devoted to variable selection methods for finding the most relevant variables for the given task. After explaining general principles, attention is paid to robust approaches, which are suitable for data contaminated by outlying values (outliers). Three main approaches to variable selection (prior, intrinsic, and posterior) are explained, and their recently proposed examples are illustrated on applications related to credit risk management and molecular genetics. These examples reveal recent robust approaches to data analysis to be able to outperform non-robust tools.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Approximate Neurocomputing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Convergence of Big Data Technologies and Computational Intelligent Techniques
ISBN
9781668452646
Number of pages of the result
25
Pages from-to
186-210
Number of pages of the book
233
Publisher name
IGI Global
Place of publication
Hershey
UT code for WoS chapter
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