Xuesong Yin

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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)
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)
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)
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 pre-processing technique(More)
When there is no sufficient labeled instances, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled instances are used to improve the performance. In this paper, we propose a dimensionality reduction method called semi-supervised TransductIve Discriminant Analysis (TIDA) which preserves the global(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 cannot necessarily lead to a better classification performance. On the other hand, DR is(More)
In this paper, in terms of pairwise constraints which specify whether a pair of instances belong to the same class (must-link constraints) or different classes (cannot-link constraints), we propose a novel semi-supervised discriminant analysis algorithm which integrates both global and local structures. Specifically, our objective is to learn a smooth as(More)