SIAM Journal on Imaging Sciences, Volume 13, Issue 2, Page 724-745, January 2020.
Supervised dictionary learning has gained much interest in the recent decade and has shown significant performance improvements in image classification. However, in general, supervised learning needs a large number of labelled samples per class to achieve an acceptable result. In order to deal with databases which have just a few labelled samples per class, semisupervised learning, which also exploits unlabelled samples in training phase is used. Indeed, unlabelled samples can help to regularize the learning model, yielding an improvement of classification accuracy. In this paper, we propose a new semisupervised dictionary learning method based on two pillars: on one hand, we enforce manifold structure preservation from the original data into sparse code space using locally linear embedding, which can be considered a regularization of sparse code; on the other hand, we train a semisupervised classifier in sparse code space. We show that our approach provides an improvement over state-of-the-art semisupervised dictionary learning methods.

中文翻译：

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具有图正则化和活动点的半监督词典学习

SIAM影像科学杂志，第13卷，第2期，第724-745页，2020年1月。
在最近的十年中，有监督的字典学习引起了人们的极大兴趣，并且在图像分类方面显示出显着的性能改进。但是，总的来说，有监督的学习需要在每个班级使用大量带标签的样本才能获得可接受的结果。为了处理每班只有几个标记样本的数据库，使用了在训练阶段也利用未标记样本的半监督学习。实际上，未标记的样本可以帮助规范学习模型，从而提高分类准确性。在本文中，我们提出了一种基于两大支柱的半监督字典学习新方法：一方面，我们使用局部线性嵌入将原始数据的流形结构保存到稀疏代码空间中，这可以视为稀疏代码的正则化；另一方面，我们在稀疏代码空间中训练了一个半监督分类器。我们表明，我们的方法比现有的半监督词典学习方法有所改进。