Robust Latent Feature Learning for Incomplete Big Data

by Di Wu

Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational...

• Format: Paperback
• Language: English
• ISBN: 9811981396
• ISBN13: 9789811981395
• Release Date: December 2022
• Publisher: Springer
• Length: 112 Pages
• Weight: 0.42 lbs.
• Dimensions: 0.3" x 6.1" x 9.2"