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Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel methods for semi-supervised clustering not only face the problem of manually tuning the kernel parameters due to the fact that no sufficient supervision is provided, but also lack a(More)
The goal of semisupervised kernel matrix learning (SS-KML) is to learn a kernel matrix on all the given samples on which just a little supervised information, such as class label or pairwise constraint, is provided. Despite extensive research, the performance of SS-KML still leaves some space for improvement in terms of effectiveness and efficiency. For(More)
miRNAs play an important role in the pathogenesis of cardiac hypertrophy and dysfunction. However, little is known about how miR-30a regulates cardiomyocyte hypertrophy. In the study, Male C57BL/6 mice were subjected to thoracic aortic constriction, and hearts were harvested at 3 weeks. We assayed miR-30a expression level by real-time PCR and defined the(More)
BACKGROUND With the rise of the burden of ischemic heart disease, both clinical and economic evidence show a desperate need to protect the heart against myocardium ischemia-reperfusion injury-related complications following cardiac surgery or percutaneous coronary intervention. However, there is no effective intervention for myocardium ischemia-reperfusion(More)
Pseudocapacitors based on fast surface Faradaic reactions can achieve high energy densities together with high power densities. Usually, researchers develop a thin layer of active materials to increase the energy density by enhancing the surface area; meanwhile, this sacrifices the mass loading. In this work, we developed a novel 3D core-shell Co3O4@Ni(OH)2(More)
The generalization ability of classification is often closely related to both the intra-class compactness and the inter-class separability. Owing to the fact that many current dimensionality reduction methods, regarded as a pre-processor, often lead to the poor classification performance on real-life data, in this paper, a new data preprocessing technique(More)
Recently, a great amount of efforts have been spent in the research of unsupervised and (semi-)supervised dimensionality reduction (DR) techniques, and DR as a preprocessor is widely applied into classification learning in practice. However, on the one hand, many DR approaches cannot necessarily lead to a better classification performance. On the other(More)