  We are surrounded by huge amounts of large-scale high dimensional data. It is
desirable to reduce the dimensionality of data for many learning tasks due to
the curse of dimensionality. Feature selection has shown its effectiveness in
many applications by building simpler and more comprehensive model, improving
learning performance, and preparing clean, understandable data. Recently, some
unique characteristics of big data such as data velocity and data variety
present challenges to the feature selection problem. In this paper, we envision
these challenges of feature selection for big data analytics. In particular, we
first give a brief introduction about feature selection and then detail the
challenges of feature selection for structured, heterogeneous and streaming
data as well as its scalability and stability issues. At last, to facilitate
and promote the feature selection research, we present an open-source feature
selection repository (scikit-feature), which consists of most of current
popular feature selection algorithms.
