This repository includes the R code for Gated Latent Beta Allocation (GLBA) and some other codes that implement baseline methods, which appeared in the following paper:
Jianbo Ye, Jia Li, Michelle Newman, Reginald B. Adams, Jr., James Z. Wang, "Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data," IEEE Transactions on Affective Computing (To appear).
This repository compiles a (ID,Score) dataset
filter_four.csv for demonstration purpose, which is a derivative of dataset EmoSet described in X. Lu's dissertation work. The use of this demo set is restricted only to explore the functionality of code.
X. Lu, “Visual characteristics for computational prediction of aesthetics and evoked emotions,” Ph.D. dissertation, The Pennsylvania State Uni- versity, 2015, chapter 5. Online available
How to run
Load data (default: valence,
the_metric = 1)
hypergraph stores the userid map
hypergraph$oracles and inverse map
Train GLBA to obtain user reliability
where the user reliability can be calculated by
Remember valence is the best dimension to obtain reliability score. Later, if you want to load arousal data, you don't need to retrain. Change
the_metric = 2 in
preprocssing.r to load arousal data.
Obtain image confidences and scores