Credit Risk Analysis Using Sparse Non-negative Matrix Factorizations

机译：使用稀疏非负矩阵分解的信用风险分析

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Credit risk analysis is to determine if a customer is likely to default on the financial obligation. In this paper, we will introduce sparse non-negative matrix factorization method to discovery the lower dimensional space for reducing the data dimensionality, which will contribute to good performance and fast computation in the credit risk classification performed by support vector machine. We test the sparse NMF in a real-world credit risk prediction task, and the empirical results demonstrate the advantage of sparse NMF by comparing with other state of art methods.

机译：信用风险分析旨在确定客户是否可能违约。在本文中，我们将引入稀疏非负矩阵分解方法来发现降低数据维数的较低维空间，这将有助于在支持向量机进行的信用风险分类中实现良好的性能和快速的计算。我们在现实世界中的信用风险预测任务中测试了稀疏NMF，并通过与其他现有技术方法进行比较，实证结果证明了稀疏NMF的优势。

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《International Conference on Information Science and Control Engineering》|2015年|181-184|共4页
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Hao Sun; Zhiqian Chen; Chen James;
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SVM; credit risk analysis; feature extraction; machine learning; non-negative matrix factorization; sparsity;

机译：支持向量机;信用风险分析;特征提取;机器学习;非负矩阵分解;稀疏性;

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4. Credit Risk Analysis Using Sparse Non-negative Matrix Factorizations [C] . Hao Sun, Zhiqian Chen, Chen James International Conference on Information Science and Control Engineering . 2015

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5. Comparing principal component analysis, singular value decomposition and non-negative matrix factorization using U.S. mortality data [D] . Hapuwitharana, J. C. 2014

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6. A unified framework for sparse non-negative least squares using multiplicative updates and the non-negative matrix factorization problem [O] . Igor Fedorov, Alican Nalci, Ritwik Giri, -1

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Credit Risk Analysis Using Sparse Non-negative Matrix Factorizations

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