Many methods have been developed for financial risk analysis. In general, the conventional unsupervised approaches lack sufficient accuracy and semantics for the clustering, and the supervised approaches rely on large amount of training data for the classification. This paper explores the semi-supervised scheme for the financial data prediction, in which accurate predictions are expected with a small amount of labeled data. Due to lack of sufficient distinguishability in financial data, it is hard for the existing semi-supervised approaches to obtain satisfactory results. In order to improve the performance, we first convert the input labeled clues to the global prior probability, and propagate the’soft’ prior probability to learn the posterior probability instead of directly propagating the’hard’ labeled data. A label diffusion model is then constructed to adaptively fuse the information at feature space and label space, which makes the structures of data affinity and labeling more consistent. Experiments on two public real financial datasets validate the effectiveness of the proposed method.

已经开发了许多用于金融风险分析的方法。一般来说，传统的无监督方法缺乏足够的聚类精度和语义，而有监督的方法依赖于大量的训练数据进行分类。本文探讨了金融数据预测的半监督方案，在该方案中，期望使用少量标记数据进行准确的预测。由于金融数据缺乏足够的可区分性，现有的半监督方法很难获得满意的结果。为了提高性能，我们首先将输入的标记线索转换为全局先验概率，并传播“软”先验概率来学习后验概率，而不是直接传播“硬”标记数据。然后构建一个标签扩散模型，自适应地融合特征空间和标签空间的信息，使数据亲和性和标签的结构更加一致。在两个公共真实金融数据集上的实验验证了所提出方法的有效性。