Variable Reduction Schemes Based on Principal Component, Canonical Correlation, and Procrustes Analyses

Toni Bakhtiar, Fanny Novika, Siswadi

Abstract



This paper was concerning with the dimension reduction over data with two multivariable sets of variables. We examined the robustness of four schemes of variable reduction with respect to the degree of information preserved by the reduced data. The proposed schemes were constructed in the framework of principal component, canonical correlation, and Procrustes analyses. Iterative procedures were then developed to subsequently removing variables with less significant contribution to the data variability pertaining to smaller first canonical correlation, smaller coefficient of canonical variable, larger correlation to principal component, and larger Procrustes goodness-of-fit. To assess the effectiveness of the schemes we considered the variable reduction problem of Barents fish data set. It is shown that reduction scheme based direct canonical correlation analysis performs effectively as it can preserve about 90.16 percent information contained by the first canonical correlation after removing 18 variables.

Keywords


Barents fish data set; canonical correlation analysis; principal component analysis; Procrustes analysis; variable reduction

Refbacks

  • There are currently no refbacks.


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.