Sampling and Learning of the And-Or Graph
- Author(s): Zhang, Ruize
- Advisor(s): WU, YINGNIAN
- et al.
The And-Or graph is a tool for knowledge representation. In this thesis we first study the
sampling of the And-Or graph with or without context constraints. Without any constraint
on the potential functions of the And-Or graph nodes, the positions and shapes of differ-
ent components of the face images are not aligned properly. In contrast, with both unary
constraints and binary constraints, the components are aligned and the samples are more
representative of the And-Or graph. We further explore parameter and structure learning of
the And-Or graph by implementing and applying some existing algorithms. The experimen-
tal results on 1D text data and 2D face image data are shown. While there is no apparent
difference between the sampling results of the parameter learned And-Or graph and the true
And-Or graph, the sampling results of the structure learned And-Or graph are not perfect
and could be further improved.