000			03628cam a22005658i 4500
001			9781003190554
003			FlBoTFG
005			20220711212505.0
006			m o d
007			cr |||||||||||
008			210317s2022 flu ob 001 0 eng
040			_aOCoLC-P _beng _erda _cOCoLC-P
020			_a9781003190554 _q(ebook)
020			_a1003190553
020			_a9781000438451 _q(electronic bk. : EPUB)
020			_a1000438457 _q(electronic bk. : EPUB)
020			_a9781000438314 _q(electronic bk. : PDF)
020			_a1000438317 _q(electronic bk. : PDF)
020			_z9781032041018 _q(hardback)
020			_z9781032041032 _q(paperback)
035			_a(OCoLC)1245248985
035			_a(OCoLC-P)1245248985
050	0	0	_aQA76.9.I52
072		7	_aBUS _x061000 _2bisacsh
072		7	_aCOM _x021030 _2bisacsh
072		7	_aCOM _x037000 _2bisacsh
072		7	_aUN _2bicssc
082	0	0	_a001.4/226 _223
100	1		_aTripathy, B. K., _d1957- _eauthor. _916981
245	1	0	_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
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			_aOCLC-licensed vendor bibliographic record.
650		0	_aInformation visualization. _914255
650		0	_aData reduction. _916982
650		0	_aMachine learning. _91831
650		7	_aBUSINESS & ECONOMICS / Statistics _2bisacsh _915543
650		7	_aCOMPUTERS / Database Management / Data Mining _2bisacsh _912290
650		7	_aCOMPUTERS / Machine Theory _2bisacsh _916983
700	1		_aS., Anveshrithaa, _eauthor. _916984
700	1		_aGhela, Shrusti, _eauthor. _916985
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003190554
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942			_cEBK
999			_c71394 _d71394