Partial least squares for discrimination
Partial least squares for discrimination
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DOI：
10.1002/cem.785
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发表时间：
2003-03-01
影响因子：
2.4
通讯作者：
Rayens, W
中科院分区：
文献类型：
--
作者：
Barker, M;Rayens, W
偏最小二乘（PLS）最初并不是作为统计判别的工具而设计的。尽管如此，应用科学家经常使用PLS进行分类，并且有大量的经验证据表明它在这一角色中表现良好。有趣的问题是：为什么一个主要为超确定回归问题设计的程序可以定位并强调群体结构？由于PLS与典型相关分析（CCA）之间的关系以及CCA与线性判别分析（LDA）之间的关系，以这种方式使用PLS具有启发式支持。本文用正式的统计解释取代了启发式。因此，当歧视是目标和降维需要时，它将变得清晰，PLS优于PCA。版权所有：John Wiley Sons， Ltd。
Partial least squares (PLS) was not originally designed as a tool for statistical discrimination. In spite of this, applied scientists routinely use PLS for classification and there is substantial empirical evidence to suggest that it performs well in that role. The interesting question is: why can a procedure that is principally designed for overdetermined regression problems locate and emphasize group structure? Using PLS in this manner has heurestic support owing to the relationship between PLS and canonical correlation analysis (CCA) and the relationship, in turn, between CCA and linear discriminant analysis (LDA). This paper replaces the heuristics with a formal statistical explanation. As a consequence, it will become clear that PLS is to be preferred over PCA when discrimination is the goal and dimension reduction is needed. Copyright (C) 2003 John Wiley Sons, Ltd.