  Context of data points, which is usually defined as the other data points in
a data set, has been found to play important roles in data representation and
classification. In this paper, we study the problem of using context of a data
point for its classification problem. Our work is inspired by the observation
that actually only very few data points are critical in the context of a data
point for its representation and classification. We propose to represent a data
point as the sparse linear combination of its context, and learn the sparse
context in a supervised way to increase its discriminative ability. To this
end, we proposed a novel formulation for context learning, by modeling the
learning of context parameter and classifier in a unified objective, and
optimizing it with an alternative strategy in an iterative algorithm.
Experiments on three benchmark data set show its advantage over
state-of-the-art context-based data representation and classification methods.
