  Online feature selection has been an active research area in recent years. We
propose a novel diverse online feature selection method based on Determinantal
Point Processes (DPP). Our model aims to provide diverse features which can be
composed in either a supervised or unsupervised framework. The framework aims
to promote diversity based on the kernel produced on a feature level, through
at most three stages: feature sampling, local criteria and global criteria for
feature selection. In the feature sampling, we sample incoming stream of
features using conditional DPP. The local criteria is used to assess and select
streamed features (i.e. only when they arrive), we use unsupervised scale
invariant methods to remove redundant features and optionally supervised
methods to introduce label information to assess relevant features. Lastly, the
global criteria uses regularization methods to select a global optimal subset
of features. This three stage procedure continues until there are no more
features arriving or some predefined stopping condition is met. We demonstrate
based on experiments conducted on that this approach yields better compactness,
is comparable and in some instances outperforms other state-of-the-art online
feature selection methods.
