Relational Latent Class Models

Posted in Science on October 17, 2008

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

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