Unsupervised learning approaches for dimensionality reduction and data visualization / (Record no. 71394)

000 -LEADER
fixed length control field 03628cam a22005658i 4500
001 - CONTROL NUMBER
control field 9781003190554
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220711212505.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210317s2022 flu ob 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781003190554
-- (ebook)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1003190553
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781000438451
-- (electronic bk. : EPUB)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1000438457
-- (electronic bk. : EPUB)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781000438314
-- (electronic bk. : PDF)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1000438317
-- (electronic bk. : PDF)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- (hardback)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- (paperback)
082 00 - CLASSIFICATION NUMBER
Call Number 001.4/226
100 1# - AUTHOR NAME
Author Tripathy, B. K.,
245 10 - TITLE STATEMENT
Title Unsupervised learning approaches for dimensionality reduction and data visualization /
250 ## - EDITION STATEMENT
Edition statement First edition.
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 online resource
520 ## - SUMMARY, ETC.
Summary, etc "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"--
700 1# - AUTHOR 2
Author 2 S., Anveshrithaa,
700 1# - AUTHOR 2
Author 2 Ghela, Shrusti,
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://www.taylorfrancis.com/books/9781003190554
856 42 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Boca Raton :
-- CRC Press Book,
-- 2022.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
520 ## - SUMMARY, ETC.
-- Provided by publisher.
588 ## -
-- OCLC-licensed vendor bibliographic record.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Information visualization.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data reduction.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine learning.
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
-- BUSINESS & ECONOMICS / Statistics
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
-- COMPUTERS / Database Management / Data Mining
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
-- COMPUTERS / Machine Theory

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