Background: A comprehensive approach to Canonical Correlation Analysis (CCA)
technique that explicitly enhances data interpretation by encountering semantic barriers in communication
Object: To the extent that there exist potential inconsistencies due to redundancy and misinterpretation
of data attributes, compatibility with respect to data interpretation may defer. For a consolidated
and technology dependent network infrastructure, the concept of inclusive CCA (such as
linear CCA, sparse CCA and kernel CCA) further asserts the inclusion of statistical correlational
analysis in semantic communication.
Methods: A Singular Value Decomposition (SVD) based Latent Semantic Indexing (LSI) method
is substantiated upon a linear dataset and simulation results are canonically analyzed for the same.
Results: Favorably, the p-value analysis from the t-test validates the significance of the application
of extensions of CCA in the field of semantic communication.
Conclusion: Hence, CCA as a statistical technique incorporates both symmetric as well as asymmetric
multivariate data analysis to help delineate the incompatibility caused due to subtle semantic-