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Can the Virtual Labels Obtained by Traditional LP Approaches Be Well Encoded in WLR?

机译:传统LP方法获得的虚拟标签能否在WLR中很好地编码?

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Semisupervised dimension reduction via virtual label regression first derives the virtual labels of unlabeled data by employing a newly designed label propagation (LP) approach (called Special random walk (SRW)) and then encodes them in a weighted linear regression model. Nie et al. (2011) highlighted two important characteristics of SRW nonexistent in the previous LP approaches: outlier detection and probability value output, which guarantee the elegant encoding of the resultant virtual labels in the weighted label regression. However, in this brief, we show that the relationship between the SRW and the previous work on LP is very close. Naturally, a problem deserving investigation is whether traditional LP approaches are indeed unable to share the above two characteristics of SRW. We aim to address this problem.
机译:通过虚拟标签回归进行的半监督降维首先通过采用新设计的标签传播(LP)方法(称为特殊随机游走(SRW))来得出未标记数据的虚拟标签,然后将它们编码在加权线性回归模型中。 Nie等。 (2011年)强调了以前的LP方法不存在的SRW的两个重要特征:离群值检测和概率值输出,这保证了加权标签回归中所得虚拟标签的优雅编码。但是,在本简介中,我们表明SRW与以前关于LP的工作之间的关系非常紧密。自然,值得研究的问题是传统的LP方法是否确实不能共享SRW的上述两个特征。我们旨在解决这个问题。

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