LEADER 03674nam 22006255 450 001 9910631083703321 005 20251009105848.0 010 $a9789811967030 010 $a9811967032 024 7 $a10.1007/978-981-19-6703-0 035 $a(MiAaPQ)EBC7141199 035 $a(Au-PeEL)EBL7141199 035 $a(CKB)25361047200041 035 $a(PPN)266350712 035 $a(DE-He213)978-981-19-6703-0 035 $a(OCoLC)1351203976 035 $a(EXLCZ)9925361047200041 100 $a20221115d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLatent Factor Analysis for High-dimensional and Sparse Matrices $eA particle swarm optimization-based approach /$fby Ye Yuan, Xin Luo 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (99 pages) 225 1 $aSpringerBriefs in Computer Science,$x2191-5776 311 08$aPrint version: Yuan, Ye Latent Factor Analysis for High-Dimensional and Sparse Matrices Singapore : Springer,c2022 9789811967023 320 $aIncludes bibliographical references and index. 327 $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. 330 $aLatent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed. 410 0$aSpringerBriefs in Computer Science,$x2191-5776 606 $aArtificial intelligence$xData processing 606 $aQuantitative research 606 $aData mining 606 $aData Science 606 $aData Analysis and Big Data 606 $aData Mining and Knowledge Discovery 615 0$aArtificial intelligence$xData processing. 615 0$aQuantitative research. 615 0$aData mining. 615 14$aData Science. 615 24$aData Analysis and Big Data. 615 24$aData Mining and Knowledge Discovery. 676 $a519.535 700 $aYuan$b Ye$0875363 702 $aLuo$b Xin 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910631083703321 996 $aLatent factor analysis for high-dimensional and sparse matrices$93083208 997 $aUNINA