Existing SSL algorithms attempt to exploit the additional information provided by the large amount of unlabeled data to guide the learning process, and enhance the final performance.
现有半监督学习算法尝试利用大量无标记数据提供的额外信息引导学习过程、提高学习性能。
参考来源 - 基于“合作—参与”计算认知模型的半监督学习算法研究与应用·2,447,543篇论文数据，部分数据来源于NoteExpress
In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data.
可以简单的理解为：非监督学习是指，尝试从未标注的数据中，寻找隐藏的结构。
Semi-supervised learning algorithms, which consider both labeled and unlabeled data, can improve learning effectiveness significantly.
半监督学习算法同时考虑有标记和无标记数据，能显著提升学习效果。
Due to the supervised view of point, most of the present tensor dimensionality reduction methods cannot take full advantage of the unlabeled data.
现有的张量维数约简方法大都是监督的，它们不能有效利用未标签样本数据的信息。
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