Canonical correlation analysis (CCA) and partial least squares (PLS) are well-known techniques for feature extraction from two sets of multidimensional variables. The fundamental difference between CCA and PLS is that CCA maximizes the correlation while PLS maximizes the covariance. Although both CCA and PLS have been applied successfully in various applications, the intrinsic relationship betw...

this study tries to expand the understanding of the relationship between transformational leadership and organizational innovation at the organizational level. this research proposes a conceptual framework to explain the components of transformational leadership while focusing on the relationship between each component and organizational innovation. a sample of 219 managers from 63 companies in...

Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. It provides better predictive ability than principle component analysis by taking into account both the independent and response variables in the dimension reduction procedure. However, PLS suffers from over-fitting problems for few samples but many variables. We formulate a ne...