Feature selection in conditional random fields for activity recognition

Temporal classification, such as activity recognition, is a key component for creating intelligent robot systems. In the case of robots, classification algorithms must robustly incorporate complex, non-independent features extracted from streams of sensor data. Conditional random fields are discriminatively trained temporal models that can easily incorporate such features. However, robots have few computational resources to spare for computing a large number of features from high bandwidth sensor data, which creates opportunities for feature selection. Creating models that contain only the most relevant features reduces the computational burden of temporal classification. In this paper, we show that lscr₁ regularization is an effective technique for feature selection in conditional random fields. We present results from a multi-robot tag domain with data from both real and simulated robots that compare the classification accuracy of models trained with lscr₁ regularization, which simultaneously smoothes the model and selects features; lscr₂ regularization, which smoothes to avoid over-fitting, but performs no feature selection; and models trained with no smoothing.