Presented by: Lu He

1. Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition Maryam Mahdaviani, Tanzeem Choudhury Presented by: Lu He

2. Challenge Collect labeled data is expensive in activity Recognition and also in many other domains

3. Challenge Training complex CRF models with large numbers of input features is slow, and exact inference is often intractable. The ability to select the most informative features as needed can reduce the computational time and the risk of over-fitting of parameters.

4. Solution A semi-supervised virtual evidence boosting (sVEB) algorithm for training Conditional Random Fields(CRFs) (Simultaneous Feature Selection and Parameter Learning)

5. Conditional Random Fields Undirected graphical models Directly represent the conditional distribution over hidden states from observations, using clique potentials Make no assumption about dependency structure between observations (different from HMM)

6. Inference & Parameter learning in CRFs Maximum likelihood Parameter Estimation: Problem: large CRFs exact inference is often intractable

7. Inference & Parameter learning in CRFs Maximum pseudo-likelihood Estimation: Problem: In some cases, over-estimate the dependency parameters

8. Boosting A kind of learning approach Sequentially learning a set of weak classifiers (weak learner) and combining them for final decisions Weak learner in VEB for training CRFs is a certain combination of features

9. Virtual Evidence • vei={ve(xi), ve(n(yi)) } • a distribution over its domain rather than a single, observed value • Two types of observations nodes: • Hard evidence ve(xi): • local observation values • Soft evidence ve(n(yi)): • messages from neighbors

10. Virtual Evidence Boosting Integrate boosting based feature selection into CRFs training Cutting a CRFs into individual patches (Like MPI), using these patches as training instances for boosting use the messages from the neighboring nodes as virtual evidence instead of using the true labels of neighbors, reducing over-estimation of neighborhood dependencies

11. Virtual Evidence Boosting The objective function: Weighted least square error(WLSE) problem:

12. Virtual Evidence Boosting

13. Virtual Evidence Boosting • Feature Selection: • For a continuous attribute x(k), the weak learner is a linear combination of decision stumps: • Fora discrete attribute x(k) ∈ {1, · · ·,D}, the weak learner is:

14. Virtual Evidence Boosting Feature Selection: 3. For a certain type of neighbor and corresponding virtual evidence vei(yk), the weak learner is the weighted sum of two indicator functions (compatibility features):

15. Virtual Evidence Boosting Feature Selection: Unify the three cases for computing optimal feature weights: Cdi is the count of feature d in data instance i, can be 0 or 1 for local features and a real number between 0 and 1 for compatibility features

16. Semi-supervised training Method: utilize unlabeled data via entropy regulation Strategy: Find labeling of the unlabeled data that mutually reinforces the supervised labels

17. Semi-supervised Virtual Evidence boosting The objective function: Weighted least square error(WLSE) problem:

18. Semi-supervised Virtual Evidence boosting

19. Experiment Synthetic Data

20. Experiment Activity dataset

21. Strength and limitation?

22. Question?