Title of article :
Modelling procedures for directed network of data blocks
Author/Authors :
Hِskuldsson، نويسنده , , Agnar، نويسنده ,
Issue Information :
دوفصلنامه با شماره پیاپی سال 2009
Pages :
8
From page :
3
To page :
10
Abstract :
Here are presented procedures for modelling data in a network. The methods are extensions of PCA or PLS regression to a forward network of data blocks. It is assumed that the data blocks are organised in a network such that one data block leads to one or more other data blocks. The procedures are stepwise ones. At each step a passage through the network is carried out. From the input weight vectors of the input or starting blocks, the score and loading vectors of all data blocks are computed. It is investigated if some score/loading vectors are not significant. If some are, they are deleted and revised estimation of the input weights are carried out. When one step is finished, all data matrices are adjusted for score and loading vectors found. A new passage through the network is carried out on the reduced matrices. If no significant loading/score vectors are found for a given set of input weights, the modelling stops. e of one data block, the algorithm reduces to PCA. In case of two data blocks it reduces to PLS regression. Most methods used in PCA or PLS regression can be applied to this procedure, e.g., cross-validation and re-sampling procedures. It is pointed out, how these methods can be used to extend other regression methods than PCA and PLS regression to a network regression.
Keywords :
Linear regression , Path models , Multi-block data , Forward network , PLS
Journal title :
Chemometrics and Intelligent Laboratory Systems
Serial Year :
2009
Journal title :
Chemometrics and Intelligent Laboratory Systems
Record number :
1489482
Link To Document :
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