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arXiv Machine Learning By Paris A. Karakasis, Walid Saad

From Common Information to Canonical Correlation Analysis and Beyond: Bridging Information Theory, Signal Processing, and Machine Learning

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The article surveys how the problem of extracting a common component from multiple jointly observed signals has been approached independently by statisticians, information theorists, and engineers. It reviews canonical correlation analysis, common information concepts, and subspace methods, then unifies these perspectives, extending to nonlinear CCA and discussing identifiability and links to self‑supervised learning. The tutorial aims to provide a self‑contained exposition that bridges signal processing, machine learning, and information theory.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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