  As a promising step, the performance of data analysis and feature learning
are able to be improved if certain pattern matching mechanism is available. One
of the feasible solutions can refer to the importance estimation of instances,
and consequently, kernel mean matching (KMM) has become an important method for
knowledge discovery and novelty detection in kernel machines. Furthermore, the
existing KMM methods have focused on concrete learning frameworks. In this
work, a novel approach to adaptive matching of kernel means is proposed, and
selected data with high importance are adopted to achieve calculation
efficiency with optimization. In addition, scalable learning can be conducted
in proposed method as a generalized solution to matching of appended data. The
experimental results on a wide variety of real-world data sets demonstrate the
proposed method is able to give outstanding performance compared with several
state-of-the-art methods, while calculation efficiency can be preserved.
