Relational Latent Class Models

author: Volker Tresp, Siemens

Categories

Top: Computer Science: Machine Learning: Principal Component Analysis

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*Slides*
0:00	Relational Latent Class Models
0:25	Overview
0:46	Relational Problems are About Networks
1:35	Relational Problems Might Involve Multiple Classes of
4:07	John Donne, 1572 – 1631
4:33	Overview: Learning with Relations (incomplete)
7:00	Statistical Relational Learning
8:11	This work
9:41	II. Before Relational Learning
9:50	IID Learning: The Matrix
10:50	Towards Relational Learning:Time Series Models
12:00	Towards Relational Learning: Hierarchical Bayesian Modeling
12:13	Learning with Related Tasks
13:26	A Hierarchical Bayesian Model
14:40	Parametric HB is too Stiff!
16:16	A Mixture Model
18:56	III Relational Modeling and Learning
19:04	Learning with Relational Data
19:18	Entity Relationship Model
20:06	Representing Ground Facts
21:00	Directed Acyclic Probabilistic Entity Relationship (DAPER) Model
21:59	DAPER and Ground Networks
23:00	Structural Learning in Relational Modeling
23:40	IV Infinite Hidden Relational Modeling
23:56	Hierarchical Bayes and Relational Learning
24:54	Relationship Prediction with Strong Attributes
25:29	Relationship Prediction with Weak (or no) User Attributes
26:20	Nonparametric Relational Bayes: Infinite Hidden Relational Model
26:37	IHRM with Parameters
26:58	The Recipe
27:41	Ground Network With an Image Structure
28:19	Ground Network With an Image Structure and Latent Variables: The IHRM
29:06	Work on Latent Class Relational Learning
30:37	The Generative Model (IHRM)
31:11	The Generative Model (MMSB)
32:30	The Generative Model (DERL)
33:04	The Generative Model (Mixed Membership DERL)
33:10	The Generative Model (Sinkkonen et al.)
34:24	Inference in the IHRM
34:58	Experiment 1: Experimental Analysis on Movie Recommendation
35:14	MovieLens Attributes
35:27	Experimental Analysis on Movie Recommendation
36:57	Movie cluster analysis Gibbs sampling with CRP
37:46	Gibbs sampling with CRP - 2
38:04	User Attributes and User Clusters
38:20	Difference to mean distribution
38:40	User Clusters versus Movie Clusters
39:14	Experiment 2: Gene Interaction and Gene Function
39:45	IHRM Model
40:58	Cluster Structure
41:56	Relevance of Attributes and Relationships
42:22	Ongoing Work: Integrate Ontology into IHRM - 1
43:03	Ongoing Work: Integrate Ontology into IHRM - 2
43:29	Experiment 3: Clinical Decision Support
44:08	IHRM Model for Clinical Decision Support
44:26	Procedure Prediction: Given First Procedure
45:27	Experiment 4: Context-Dependent Statistical Trust Learning
46:42	Infinite Hidden Relational Trust Model
47:40	eBay Data
49:34	Conclusion
51:07	- Questions

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