MARC Record from marc_columbia

LEADER: 04280cam a2200697 i 4500
001 15672042
005 20220507233301.0
006 m o d
007 cr |||||||||||
008 210317s2022 flu ob 001 0 eng
010 $a 2021012897
035 $a(OCoLC)on1245248985
035 $a(NNC)15672042
040 $aDLC$beng$erda$cDLC$dOCLCF$dTYFRS$dDLC$dOCLCO
020 $a9781003190554$q(ebook)
020 $a1003190553
020 $z9781032041018$q(hardback)
020 $z9781032041032$q(paperback)
020 $a9781000438451$q(electronic bk. : EPUB)
020 $a1000438457$q(electronic bk. : EPUB)
020 $a9781000438314$q(electronic bk. : PDF)
020 $a1000438317$q(electronic bk. : PDF)
035 $a(OCoLC)1245248985
037 $a9781003190554$bTaylor & Francis
042 $apcc
050 00 $aQA76.9.I52
072 7 $aBUS$x061000$2bisacsh
072 7 $aCOM$x021030$2bisacsh
072 7 $aCOM$x037000$2bisacsh
072 7 $aUN$2bicssc
082 00 $a001.4/226$223
049 $aZCUA
100 1 $aTripathy, B. K.,$d1957-$eauthor.
245 10 $aUnsupervised learning approaches for dimensionality reduction and data visualization /$cB.K. Tripathy, Anveshrithaa S, Shrusti Ghela.
250 $aFirst edition.
264 1 $aBoca Raton :$bCRC Press Book,$c2022.
300 $a1 online resource
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
504 $aIncludes bibliographical references and index.
520 $a"This book describes algorithms like Locally Linear Embedding (LLE), Laplacian eigenmaps, Isomap, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed including strengths and the limitations. It highlights important use cases of these algorithms and few examples along with visualizations. Comparative study of the algorithms is presented, to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization. Features: Demonstrates how unsupervised learning approaches can be used for dimensionality reduction. Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts. Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use. Provides use cases, illustrative examples, and visualizations of each algorithm. Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis. This book aims at professionals, graduate students and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction"--$cProvided by publisher.
588 $aDescription based on print version record and CIP data provided by publisher.
650 0 $aInformation visualization.
650 0 $aData reduction.
650 0 $aMachine learning.
650 6 $aVisualisation de l'information.
650 6 $aRéduction des données (Statistique)
650 6 $aApprentissage automatique.
650 7 $aBUSINESS & ECONOMICS / Statistics$2bisacsh
650 7 $aCOMPUTERS / Database Management / Data Mining$2bisacsh
650 7 $aCOMPUTERS / Machine Theory$2bisacsh
650 7 $aData reduction.$2fast$0(OCoLC)fst00887976
650 7 $aInformation visualization.$2fast$0(OCoLC)fst00973185
650 7 $aMachine learning.$2fast$0(OCoLC)fst01004795
655 4 $aElectronic books.
700 1 $aS., Anveshrithaa,$eauthor.
700 1 $aGhela, Shrusti,$eauthor.
776 08 $iPrint version:$aTripathy, B. K., 1957-$tUnsupervised learning approaches for dimensionality reduction and data visualization$bFirst edition.$dBoca Raton : CRC Press Book, 2022.$z9781032041018$w(DLC) 2021012896
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15672042$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS