Regularized Partial Least Squares with an Application to NMR Spectroscopy.

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Statistical Analysis and Data Mining Pub Date : 2013-08-01 DOI:10.1002/sam.11169

Genevera I Allen, Christine Peterson, Marina Vannucci, Mirjana Maletić-Savatić

引用次数: 45

Abstract

High-dimensional data common in genomics, proteomics, and chemometrics often contains complicated correlation structures. Recently, partial least squares (PLS) and Sparse PLS methods have gained attention in these areas as dimension reduction techniques in the context of supervised data analysis. We introduce a framework for Regularized PLS by solving a relaxation of the SIMPLS optimization problem with penalties on the PLS loadings vectors. Our approach enjoys many advantages including flexibility, general penalties, easy interpretation of results, and fast computation in high-dimensional settings. We also outline extensions of our methods leading to novel methods for non-negative PLS and generalized PLS, an adoption of PLS for structured data. We demonstrate the utility of our methods through simulations and a case study on proton Nuclear Magnetic Resonance (NMR) spectroscopy data.

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正则化偏最小二乘在核磁共振光谱中的应用。

基因组学、蛋白质组学和化学计量学中常见的高维数据通常包含复杂的相关结构。近年来，偏最小二乘(PLS)和稀疏PLS方法作为监督数据分析中的降维技术在这些领域得到了广泛的关注。我们通过解决对PLS加载向量进行惩罚的SIMPLS优化问题的松弛，引入了正则化PLS的框架。我们的方法具有许多优点，包括灵活性、一般惩罚、易于解释结果以及在高维环境下的快速计算。我们还概述了我们的方法的扩展，导致非负PLS和广义PLS的新方法，采用结构化数据的PLS。我们通过模拟和质子核磁共振(NMR)光谱数据的案例研究证明了我们的方法的实用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.20

自引率

7.70%

发文量

期刊介绍： Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.