Developing the maximum likelihood estimation for component analysis: A state space approach

开发成分分析的最大似然估计：状态空间方法

• 批准号: RGPIN-2016-04779
• 负责人: Gu, Fei
• 金额: $ 0.61万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636009
• 关键词: Developing; maximum; likelihood; estimation; component

Traditionally, scientists have used component analysis (CA) as descriptive tools in exploratory data analysis. Mathematically, a component is defined as a linear combination of the observed variables. The prototype of CA is principal component analysis. Other basic methods of CA include canonical correlation analysis and redundancy analysis. Over the past several decades, various generalizations of these basic component models were developed, including simultaneous component analysis, three-way component analysis, multiple-set canonical correlation analysis, and extended redundancy analysis, just to name a few.

传统上，科学家们使用成分分析（CA）作为探索性数据分析的描述性工具。在数学上，分量被定义为观测变量的线性组合。元胞自动机的原型是主成分分析。CA的其他基本方法包括典型相关分析和冗余分析。在过去的几十年里，这些基本的组件模型的各种推广，包括同步组件分析，三向组件分析，多组典型相关分析，和扩展冗余分析，仅举几例。