Abstract: In this paper, the finite-time symplectic synchronization
between two different chaotic systems is investigated. Based on the
finite-time stability theory, a simple adaptive feedback scheme is
proposed to realize finite-time symplectic synchronization for the
Lorenz and L¨u systems. Numerical examples are provided to show
the effectiveness of the proposed method.
Abstract: Sparse representation which can represent high dimensional
data effectively has been successfully used in computer vision
and pattern recognition problems. However, it doesn-t consider the
label information of data samples. To overcome this limitation,
we develop a novel dimensionality reduction algorithm namely
dscriminatively regularized sparse subspace learning(DR-SSL) in this
paper. The proposed DR-SSL algorithm can not only make use of
the sparse representation to model the data, but also can effective
employ the label information to guide the procedure of dimensionality
reduction. In addition,the presented algorithm can effectively deal
with the out-of-sample problem.The experiments on gene-expression
data sets show that the proposed algorithm is an effective tool for
dimensionality reduction and gene-expression data classification.