For classification tasks, dictionary learning based methods have attracted lots of attention in recent years. One popular way to achieve this purpose is introduce label information generate a discriminative represent samples. However, compared with traditional learning, category only achieves significant improvements supervised and has little positive influence on semi-supervised or unsupervise...

Community structure is vital to discover the important structures and potential property of complex networks. In recent years, the increasing quality of local community detection approaches has become a hot spot in the study of complex network due to the advantages of linear time complexity and applicable for large-scale networks. However, there are many shortcomings in these methods such as in...

A catalysis-based, label-free, and high-throughput colorimetric protocol has been initially proposed for detecting mercury(II) in blood and wastewater with 96-cell plates, based on the mercury-enhanced catalytic activity of small silver nanoparticles synthesized in a gelatin matrix with unique temperature switchable sol-gel transition.

We propose a framework to incorporate unlabeled data in kernel classifier, based on the idea that two points in the same cluster are more likely to have the same label. This is achieved by modifying the eigenspectrum of the kernel matrix. Experimental results assess the validity of this approach.

In this work, we addressed the issue of improving the classification quality of label pairwise ensembles. Our goal is to improve the classification quality achieved by the ensemble via modification of the base classifiers that constitute the ensemble. To achieve this goal, a correction procedure that computes the measures of competence and cross-competence of a single classifier is proposed. Th...