Xian-Fa Cai

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Precise cancer classification is essential to the successful diagnosis and treatment of cancers. Although semisupervised dimensionality reduction approaches perform very well on clean datasets, the topology of the neighborhood constructed with most existing approaches is unstable in the presence of high-dimensional data with noise. In order to solve this(More)
A well-designed graph plays a fundamental role in graph-based semi-supervised learning; however, the topological structure of a constructed neighborhood is unstable in most current approaches, since they are very sensitive to the high dimensional, sparse and noisy data. This generally leads to dramatic performance degradation. To deal with this issue, we(More)
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