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Canonical Correlation Analysis
2012Canonical correlation analysis (CCA) is one of the most important tools of multivariate statistical analysis. Its extension to the functional context is not trivial, and in many ways illustrates the differences between multivariate and functional data. One of the most influential contributions has been made by Leurgans et al.
Lajos Horváth, Piotr Kokoszka
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Canonical Correlation Analysis
2003Complex multivariate data structures are better understood by studying low-dimensional projections. For a joint study of two data sets, we may ask what type of low-dimensional projection helps in finding possible joint structures for the two samples.
Wolfgang Karl Härdle, Léopold Simar
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Canonical correlations and canonical variates
1985In this chapter we shall summarize the essential elements of the theory of canonical correlations and variates. We shall begin by formulating the problem. The derivation of canonical correlations and canonical variates will then be taken up. Canonical analysis can be derived in several ways.
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Canonical Correlation Analysis
2019In many applications, one wants to associate one kind of data with another. For example, every data item could be a video sequence together with its sound track. You might want to use this data to learn to associate sounds with video, so you can predict a sound for a new, silent, video.
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Canonical Correlation Analysis
2017This chapter covers classical and robust canonical correlation analysis (CCA). Let \(\varvec{x}\) be the \(p \times 1\) vector of predictors, and partition \({\varvec{x}} = ({\varvec{w}}^T, {\varvec{y}}^T)^T\) where \({\varvec{w}}\) is \(m \times 1\) and \({\varvec{y}}\) is \(q \times 1\) with \(m = p-q \le q\) and \(m, q \ge 1\). If \(m = 1\) and \(q =
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