  In this paper, a new classifier based on the intrinsic properties of the data
is proposed. Classification is an essential task in data mining-based
applications. The classification problem will be challenging when the size of
the training set is not sufficient to compare to the dimension of the problem.
This paper proposes a new classification method that exploits the intrinsic
structure of each class through the corresponding Eigen components. Each
component contributes to the learned span of each class by specific weight. The
weight is determined by the associated eigenvalue. This approach results in
reliable learning robust in the case of facing a classification problem with
limited training data. The proposed method involves the obtained Eigenvectors
by SVD of data from each class to select the bases for each subspace. Moreover,
it considers an efficient weighting for the decision-making criterion to
discriminate two classes. In addition to high performance on artificial data,
this method has increased the best result of international competition.
