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024	7		_a10.1007/978-981-19-6703-0 _2doi
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100	1		_aYuan, Ye. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aLatent Factor Analysis for High-dimensional and Sparse Matrices _h[electronic resource] : _bA particle swarm optimization-based approach / _cby Ye Yuan, Xin Luo.
250			_a1st ed. 2022.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022.
300			_aVIII, 92 p. 1 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Computer Science, _x2191-5776
505	0		_aChapter 1. Introduction -- Chapter 2. Learning rate-free Latent Factor Analysis via PSO -- Chapter 3. Learning Rate and Regularization Coefficient-free Latent Factor Analysis via PSO -- Chapter 4. Regularization and Momentum Coefficient-free Non-negative Latent Factor Analysis via PSO -- Chapter 5. Advanced Learning rate-free Latent Factor Analysis via P2SO -- Chapter 6. Conclusion and Discussion.
520			_aLatent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.
650		0	_aArtificial intelligence _xData processing.
650		0	_aQuantitative research.
650		0	_aData mining.
650	1	4	_aData Science.
650	2	4	_aData Analysis and Big Data.
650	2	4	_aData Mining and Knowledge Discovery.
700	1		_aLuo, Xin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789811967023
776	0	8	_iPrinted edition: _z9789811967047
830		0	_aSpringerBriefs in Computer Science, _x2191-5776
856	4	0	_uhttps://doi.org/10.1007/978-981-19-6703-0
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