國立虎尾科技大學 |

Dimensionality Reduction in Data Science

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Dimensionality Reduction in Data Science/ edited by Max Garzon, Ching-Chi Yang, Deepak Venugopal, Nirman Kumar, Kalidas Jana, Lih-Yuan Deng.
其他作者:	Deng, Lih-Yuan.
面頁冊數:	XI, 265 p. 62 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Biomedical Engineering and Bioengineering. -
電子資源:	https://doi.org/10.1007/978-3-031-05371-9
ISBN:	9783031053719

Dimensionality Reduction in Data Science
Dimensionality Reduction in Data Science[electronic resource] /edited by Max Garzon, Ching-Chi Yang, Deepak Venugopal, Nirman Kumar, Kalidas Jana, Lih-Yuan Deng. - 1st ed. 2022. - XI, 265 p. 62 illus.online resource.

1. What is Data Science (DS)? -- 2. Solutions to Data Science Problems -- 3. What is Dimensionality Reduction (DR)? -- 4. Conventional Statistical Approaches -- 5. Geometric Approaches -- 6. Information-theoretic Approaches -- 7. Molecular Computing Approaches -- 8. Statistical Learning Approaches -- 9. Machine Learning Approaches -- 10. Metaheuristics of DR Methods -- 11. Appendices.

This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated. The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains. This book focuses on data science and problem definition, data cleansing, feature selection and extraction, statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting. This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.

ISBN: 9783031053719

Standard No.: 10.1007/978-3-031-05371-9doiSubjects--Topical Terms:

1211019
Biomedical Engineering and Bioengineering.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Dimensionality Reduction in Data Science
LDR:03718nam a22004215i 4500 001 1089070
003 DE-He213
005 20220728171801.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783031053719 $9 978-3-031-05371-9
024 7 $a 10.1007/978-3-031-05371-9 $2 doi
035 $a 978-3-031-05371-9
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a UYQE $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
245 1 0 $a Dimensionality Reduction in Data Science $h [electronic resource] / $c edited by Max Garzon, Ching-Chi Yang, Deepak Venugopal, Nirman Kumar, Kalidas Jana, Lih-Yuan Deng.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a XI, 265 p. 62 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 1. What is Data Science (DS)? -- 2. Solutions to Data Science Problems -- 3. What is Dimensionality Reduction (DR)? -- 4. Conventional Statistical Approaches -- 5. Geometric Approaches -- 6. Information-theoretic Approaches -- 7. Molecular Computing Approaches -- 8. Statistical Learning Approaches -- 9. Machine Learning Approaches -- 10. Metaheuristics of DR Methods -- 11. Appendices.
520 $a This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated. The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains. This book focuses on data science and problem definition, data cleansing, feature selection and extraction, statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting. This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.
650 2 4 $a Biomedical Engineering and Bioengineering. $3 1211019
650 2 4 $a Machine Learning. $3 1137723
650 1 4 $a Data Mining and Knowledge Discovery. $3 677765
650 0 $a Biomedical engineering. $3 588770
650 0 $a Machine learning. $3 561253
650 0 $a Data mining. $3 528622
700 1 $a Deng, Lih-Yuan. $e editor. $1 https://orcid.org/0000-0002-7350-9751 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1396312
700 1 $a Jana, Kalidas. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1396311
700 1 $a Kumar, Nirman. $e editor. $1 https://orcid.org/0000-0001-6601-2790 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1396310
700 1 $a Venugopal, Deepak. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1396309
700 1 $a Yang, Ching-Chi. $e editor. $1 https://orcid.org/0000-0003-2517-2709 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1396308
700 1 $a Garzon, Max. $e editor. $1 https://orcid.org/0000-0001-9552-2255 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1396307
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783031053702
776 0 8 $i Printed edition: $z 9783031053726
776 0 8 $i Printed edition: $z 9783031053733
856 4 0 $u https://doi.org/10.1007/978-3-031-05371-9
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)