  In this paper, we study the problem of using contextual da- ta points of a
data point for its classification problem. We propose to represent a data point
as the sparse linear reconstruction of its context, and learn the sparse
context to gather with a linear classifier in a su- pervised way to increase
its discriminative ability. We proposed a novel formulation for context
learning, by modeling the learning of context reconstruction coefficients and
classifier in a unified objective. In this objective, the reconstruction error
is minimized and the coefficient spar- sity is encouraged. Moreover, the hinge
loss of the classifier is minimized and the complexity of the classifier is
reduced. This objective is opti- mized by 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.
