PLS approach for clusterwise linear regression on functional data

被引：0
|
作者：
Preda, C [1 ]
Saporta, G [1 ]
机构：
[1] Univ Lille 2, Lille, France
来源：
CLASSIFICATION, CLUSTERING, AND DATA MINING APPLICATIONS | 2004年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Partial Least Squares (PLS) approach is used for the clusterwise linear regression algorithm when the set of predictor variables forms an L-2-continuous stochastic process. The number of clusters is treated as unknown and the convergence of the clusterwise algorithm is discussed. The approach is compared with other methods via an application to stock-exchange data.
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页码：167 / 176
页数：10
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