Suitability of CCA for Generating Latent State/ Variables in Multi-View Textual Data

arXiv:2406.12997v1 Announce Type: new
Abstract: The probabilistic interpretation of Canonical Correlation Analysis (CCA) for learning low-dimensional real vectors, called as latent variables, has been exploited immensely in various fields. This study takes a step further by demonstrating the potential of CCA in discovering a latent state that captures the contextual information within the textual data under a two-view setting. The interpretation of CCA discussed in this study utilizes the multi-view nature of textual data, i.e. the consecutive sentences in a …

abstract analysis arxiv canonical correlation cs.cl data fields interpretation low potential state study textual type variables vectors view