Tools

LEADER 03644nam a22005178i 4500

001 ssj0002482808;

003 WaSeSS;

005 20251212090320.0;

006 m d ;

007 cr n ;

008 210317s2022 flu sb 001 0 eng d;

010 a| 2021012896;

020 a| 9781032041018q| (hardback);

020 a| 9781032041032q| (paperback);

020 z| 9781003190554q| (ebook);

035 a| (WaSeSS)ssj0002482808;

040 a| DLCb| engc| DLCd| WaSeSS;

042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| QA76.9.I52b| T75 2022;

082 00 a| 001.4/2262| 23;

100 1 a| Tripathy, B. K.,d| 1957-=| ^A1392954;

245 10 a| Unsupervised learning approaches for dimensionality reduction and data visualizationh| [electronic resource] /c| B.K. Tripathy, Anveshrithaa S, Shrusti Ghela.;

250 a| First edition.;

260 a| Boca Raton :b| CRC Press Book,c| 2022.;

263 a| 2109;

300 a| pages cm;

504 a| Includes bibliographical references and index.;

506 a| Available only to authorized users.;

520 a| "This book describes algorithms like Locally Linear Embedding (LLE), Laplacian eigenmaps, Isomap, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed including strengths and the limitations. It highlights important use cases of these algorithms and few examples along with visualizations. Comparative study of the algorithms is presented, to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization. Features: Demonstrates how unsupervised learning approaches can be used for dimensionality reduction. Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts. Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use. Provides use cases, illustrative examples, and visualizations of each algorithm. Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis. This book aims at professionals, graduate students and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Information visualization.=| ^A595771;

650 0 a| Data reduction.=| ^A425626;

650 0 a| Machine learning.=| ^A145211;

655 0 a| Electronic books.=| ^A491897;

700 1 a| S., Anveshrithaa.?| UNAUTHORIZED;

700 1 a| Ghela, Shrusti.?| UNAUTHORIZED;

856 40 z| Full text available from Taylor & Francis eBooksu| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.taylorfrancis.com%2Fbooks%2F9781003190554;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

949 a| CLICK ON WEB ADDRESSw| ASISh| HSL77;

949 a| CLICK ON WEB ADDRESSw| ASISh| JMUSIC60;

596 a| 1 3 4;

998 a| 6691152;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6691152-1001l| JNETm| JOYNERr| Ys| Yt| JNESSBKu| 12/19/2025x| EBOOKz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6691152-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 12/19/2025x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6691152-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 12/19/2025x| EBOOKz| JERESOURCE;

Unsupervised learning approaches for dimensionality reduction and data visualization / B.K. Tripathy, Anveshrithaa S, Shrusti Ghela.

Author/creator	Tripathy, B. K., 1957-
Other author	S., Anveshrithaa.
Other author	Ghela, Shrusti.
Format	Electronic
Edition	First edition.
Publication Info	Boca Raton : CRC Press Book, 2022.
Description	pages cm
Supplemental Content	Full text available from Taylor & Francis eBooks
Subjects	Information visualization. Data reduction. Machine learning.

Abstract	"This book describes algorithms like Locally Linear Embedding (LLE), Laplacian eigenmaps, Isomap, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed including strengths and the limitations. It highlights important use cases of these algorithms and few examples along with visualizations. Comparative study of the algorithms is presented, to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization. Features: Demonstrates how unsupervised learning approaches can be used for dimensionality reduction. Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts. Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use. Provides use cases, illustrative examples, and visualizations of each algorithm. Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis. This book aims at professionals, graduate students and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2021012896
ISBN	9781032041018 (hardback)
ISBN	9781032041032 (paperback)
ISBN	(ebook)

Availability

Library	Location	Call Number	Status	Item Actions
	Electronic Resources	Access Content Online	✔ Available

Unsupervised learning approaches for dimensionality reduction and data visualization / B.K. Tripathy, Anveshrithaa S, Shrusti Ghela.

Click here for more information about this title

Availability