Abstract
Background: A comprehensive approach to Canonical Correlation Analysis (CCA) technique that explicitly enhances data interpretation by encountering semantic barriers in communication is proposed.
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- defects.
Keywords: Data compatibility, semantic, sparse CCA, kernel CCA, linear CCA, multivariate, latent semantic indexing, singular value decomposition, t-test.
Graphical Abstract
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