  Online selection of dynamic features has attracted intensive interest in
recent years. However, existing online feature selection methods evaluate
features individually and ignore the underlying structure of feature stream.
For instance, in image analysis, features are generated in groups which
represent color, texture and other visual information. Simply breaking the
group structure in feature selection may degrade performance. Motivated by this
fact, we formulate the problem as an online group feature selection. The
problem assumes that features are generated individually but there are group
structure in the feature stream. To the best of our knowledge, this is the
first time that the correlation among feature stream has been considered in the
online feature selection process. To solve this problem, we develop a novel
online group feature selection method named OGFS. Our proposed approach
consists of two stages: online intra-group selection and online inter-group
selection. In the intra-group selection, we design a criterion based on
spectral analysis to select discriminative features in each group. In the
inter-group selection, we utilize a linear regression model to select an
optimal subset. This two-stage procedure continues until there are no more
features arriving or some predefined stopping conditions are met. %Our method
has been applied Finally, we apply our method to multiple tasks including image
classification %, face verification and face verification. Extensive empirical
studies performed on real-world and benchmark data sets demonstrate that our
method outperforms other state-of-the-art online feature selection %method
methods.
