Relational Latent Class Models

Lecture slides:

• Relational Latent Class Models
• Overview
• Relational Problems are About Networks
• Relational Problems Might Involve Multiple Classes of
• John Donne, 1572 – 1631
• Overview: Learning with Relations (incomplete)
• Statistical Relational Learning
• This work
• II. Before Relational Learning
• IID Learning: The Matrix
• Towards Relational Learning:Time Series Models
• Towards Relational Learning: Hierarchical Bayesian Modeling
• Learning with Related Tasks
• A Hierarchical Bayesian Model
• Parametric HB is too Stiff!
• A Mixture Model
• III Relational Modeling and Learning
• Learning with Relational Data
• Entity Relationship Model
• Representing Ground Facts
• Directed Acyclic Probabilistic Entity Relationship (DAPER) Model
• DAPER and Ground Networks
• Structural Learning in Relational Modeling
• IV Infinite Hidden Relational Modeling
• Hierarchical Bayes and Relational Learning
• Relationship Prediction with Strong Attributes
• Relationship Prediction with Weak (or no) User Attributes
• Nonparametric Relational Bayes: Infinite Hidden Relational Model
• IHRM with Parameters
• The Recipe
• Ground Network With an Image Structure
• Ground Network With an Image Structure and Latent Variables: The IHRM
• Work on Latent Class Relational Learning
• The Generative Model (IHRM)
• The Generative Model (MMSB)
• The Generative Model (DERL)
• The Generative Model (Mixed Membership DERL)
• The Generative Model (Sinkkonen et al.)
• V Making it all work
• Inference in the IHRM
• Experiment 1: Experimental Analysis on Movie Recommendation
• MovieLens Attributes
• Experimental Analysis on Movie Recommendation
• Movie cluster analysis Gibbs sampling with CRP
• Gibbs sampling with CRP - 2
• User Attributes and User Clusters
• Difference to mean distribution
• User Clusters versus Movie Clusters
• Experiment 2: Gene Interaction and Gene Function
• IHRM Model
• Cluster Structure
• Relevance of Attributes and Relationships
• Ongoing Work: Integrate Ontology into IHRM - 1
• Ongoing Work: Integrate Ontology into IHRM - 2
• Experiment 3: Clinical Decision Support
• IHRM Model for Clinical Decision Support
• Procedure Prediction: Given First Procedure
• Experiment 4: Context-Dependent Statistical Trust Learning
• Infinite Hidden Relational Trust Model
• eBay Data
• Predictive Performance
• Conclusion

Author: Volker Tresp, Siemens