Training Conditional Random Fields using Virtual Evidence Boosting Lin Liao, Tanzeem Choudhury † , Dieter Fox, and Henr

1. Experiments Algorithms Context sequence Activity sequence Training Conditional Random Fields using Virtual Evidence Boosting Lin Liao, Tanzeem Choudhury†, Dieter Fox, and Henry Kautz University of Washington †Intel Research Introduction Goal:To develop efficient feature selection and parameter estimation technique for Conditional Random Fields (CRFs) Application domain: To learn human activity models from continuous, multi-modal sensory inputs Approaches to Training Conditional Random Fields (CRFs) • Maximum Likelihood • Run numerical optimization to find the optimal weights, which requires inference at each iteration • Inefficient for complex structures • Inadequate for continuous observations and feature selection • Maximum Pseudo-Likelihood • Convert a CRF into separate patches; each consists of a hidden node and true values of neighbors • Run ML learning on separate patches • Efficient but may over-estimate inter-dependency • Inadequate for continuous observations and feature selection • Our Approach: Virtual Evidence Boosting • Convert a CRF into separate patches; each consists of a hidden node and virtual evidence of neighbors • Run boosting (to select features) and belief propagation (to update virtual evidence) alternately • Efficient and unified approach to feature selection and parameter estimation • Suitable for both discrete and continuous observations Extension of LogitBoost with Virtual Evidence Virtual Evidence Boosting for CRFs • Traditional boosting algorithms assume feature values be deterministic • We extend LogitBoost algorithm to handle virtual evidence, i.e., a feature could also be a likelihood value or probability distribution INPUTS: Structure of CRF and training samples OUTPUT: F (linear combination of features) FOR each iteration Run BP using current F to get virtual evidence ve(xi, n(yi)); FOR each sample Compute likelihood Compute sample weight Compute working response END Obtain best weak learner by solving Add the weak learner to F END INPUTS: training samples OUTPUT: F (linear combination of features) FOR each iteration FOR each sample Compute likelihood Compute sample weight Compute working response END Obtain best weak learner by solving Add the weak learner to F END • Boosted Random Fields versus VEB • Closest related work to VEB is Boosted Random Fields (Torralba 2004) • BRFs combine boosting and belief propagation but assume dense graph structure and weak pair-wise influence • We compare the two as the pair-wise influence changes • VEB performs significantly better with strong relations Application: Human Activity Recognition Model human activities and select discriminatory features from multimodal sensor data. Sensors include accelerometer, audio, light, temperature, etc. • Indoor Activities • Activities: computer usage, meal, TV, meeting, and sleeping • Linear chain CRF with 315 continuous input features • 1100 minutes of data over 12 days Physical Activities and Spatial Contexts Feature Selection VEB can be used to extract sparse structure from complex models. In this experiment it is able to find the exact order in a high-order HMM, and thus outperforms other learning alternatives. • Context: indoors, outdoors, and vehicles • Activities: stationary, walking, running, driving, and going up/down stairs • Approximately 650 continuous input features • 400 minutes of data over 12 episodes