一种融合T-Rank和Softmax的特征提取算法研究

The Research of Feature Extraction Algorithm by Integrating T-Rank and Softmax Methods

摘要: 本文针对高维生物数据特征提出了一种融合T-Rank和Softmax的特征提取算法。该方法比传统特征提取方法在处理高维生物数据更加有效，不仅提取的特征个数较少，而且计算速度快。利用算法本文对高维银屑病基因表达谱数据进行了研究，得到了分类准确率较高的疾病诊断模型。

Abstract: The paper proposed a new feature extraction algorithm by integrating T-rank and Softmax for the high dimensional biological data sets, which is more effective than traditional method when dealing with high dimensional data. It can not only extract a very few number of features, but also have fast computing speed. By using of this new algorithm, the paper obtains a high accuracy diagnosis model for psoriasis.