Verkauf durch Sack Fachmedien

Luo / Yuan

Latent Factor Analysis for High-dimensional and Sparse Matrices

A particle swarm optimization-based approach

Medium: Buch
ISBN: 978-981-19-6702-3
Verlag: Springer Nature Singapore
Erscheinungstermin: 16.11.2022
Lieferfrist: bis zu 10 Tage

Latent 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.


Produkteigenschaften


  • Artikelnummer: 9789811967023
  • Medium: Buch
  • ISBN: 978-981-19-6702-3
  • Verlag: Springer Nature Singapore
  • Erscheinungstermin: 16.11.2022
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2022
  • Serie: SpringerBriefs in Computer Science
  • Produktform: Kartoniert
  • Gewicht: 166 g
  • Seiten: 92
  • Format (B x H x T): 155 x 235 x 6 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

Chapter 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 PSO.- Chapter 6. Conclusion and Discussion.