[IROS'07]Feature Selection in Conditional Random Fields for Activity Recognition

Title: Feature Selection in Conditional Random Fields for Activity Recognition

Author:
Vail, Douglas Carnegie Mellon Univ.
Lafferty, John Carnegie Mellon Univ.
Veloso, Manuela Carnegie Mellon Univ.

Abstract:

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 l1 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 l1 regularization,
which simultaneously smoothes the model and selects features;
l2 regularization, which smoothes to avoid over-fitting, but
performs no feature selection; and models trained with no
smoothing.