Research on Latent Factor Model and its Optimization Algorithms of Machine Learning

Qing Feng Li

doi:10.4028/www.scientific.net/AMM.734.495

Paper Titles

Particle Filter Target Tracking Algorithm Based on the SIFT and Color Features Fusion
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Reentry Trajectory Optimization Based on Time-Domain Improved Particle Swarm Optimization
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Research of MES Scheduling Strategy Based on Hypercube Paths Link Algorithm
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Research of the Algorithms for Service Restoration Reconfiguration in Distribution Network
p.491

Research on Latent Factor Model and its Optimization Algorithms of Machine Learning
p.495

Research on the Optimization Model of Flights Alternate Based on Linear Programming
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Research on Unconstrained Handwritten Numeral Recognition by BP Feature Screening Based on Fuzzy Clustering
p.504

Short-Term Traffic Flow Forecasting Based on SVR with Improved Artificial Fish Swarm Algorithm
p.508

Study on the Prediction Meth of Automobile Braking Distance Based on Artificial Neural Network
p.515

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 734Research on Latent Factor Model and its...

Research on Latent Factor Model and its Optimization Algorithms of Machine Learning

Abstract:

There are some bottleneck problems in the supervised machine learning and unsupervised machine learning. In view of the current problems, this paper tries to make some meaningful exploration. The main work is as follows: Research on the statistical analysis of factor analysis and latent variable and in some valuable research results of typical machine learning, and some no analysis method and factor analysis of supervised learning or hidden variables method to contact with the typical analysis, summary of the comprehensive characteristics of implicit factor model and to reveal the hiding data structures help and contributions.