Sequential method is promising for feature selection in high-dimensional models. In this paper, we propose a sequential approach based on partial profile score dubbed as PPSFS to feature selection for a broad class of high-dimensional models, including high-dimensional generalized linear interaction models. The PPSFS approach has a prominent performance in feature selection while it keeps highly scalable for ultra-high-dimensional models. The selection consistency of the PPSFS approach is established under mild conditions. Comprehensive numerical studies demonstrating the performance of PPSFS are reported. A real data analysis for gene expression cancer RNA-Seq data is also presented.

中文翻译：

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高维广义线性交互模型中的部分轮廓得分特征选择

顺序方法在高维模型中的特征选择很有前景。在本文中，我们提出了一种基于部分轮廓分数的顺序方法，称为 PPSFS，用于为广泛的高维模型（包括高维广义线性交互模型）进行特征选择。PPSFS 方法在特征选择方面具有突出的性能，同时对超高维模型保持高度可扩展性。PPSFS 方法的选择一致性是在温和条件下建立的。报告了展示 PPSFS 性能的综合数值研究。还提供了基因表达癌症 RNA-Seq 数据的真实数据分析。