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

Paris A. Karakasis, Walid Saad · 2026 · Entropy

DOI: 10.3390/e28101161 · cited by 0 works (OpenAlex) · open-access PDF

Arabic

العديد من المشكلات في معالجة الإشارات، التعلم الآلي، والتصوير العصبي توفر العديد من التقييمات المشتركة للعمليات، كل منها تجمع جزءًا مشتركًا مع جزءًا مشتركًا مع نفسها، وبالتالي تتطلب جزءًا مشتركًا يمكن استرداده دون مراقبة. وقد تم دراسة هذا المشكلة في العديد من المجتمعات، بشكل كبير بشكل مستقل. قدمت الإحصائيات نتائجًا رئيسية وأدوات كل من تحليل التوازن الكوني (CCA) التي تم تطويرها من قبل Hotelling في عام 1936، وشرح الاتصالات التي تنشأ في مكان واحد، وذلك بسبب Gács وKörner و Wyner. تم تطوير المهندسين طرقًا ثانويًا لتقنيات التخزين والاتصالات متعددة المستخدمين لت

Machine translation (Meta M2M100); the English original follows.

Abstract

Many problems in signal processing, machine learning, and neuroimaging provide several jointly observed measurements of one process, each mixing a component common to all of them with a component private to itself, and require the common component to be recovered without supervision. This problem has been studied in several communities, largely independently. Statisticians proposed canonical correlation analysis (CCA), introduced by Hotelling in 1936. Information theorists proposed two notions of common information, due to Gács and Körner and to Wyner. Engineers developed subspace methods for sensor arrays and multiuser communications to design multiview receivers. This tutorial presents the main results and tools each community developed for this problem in a single exposition, and explains the connections that emerge where they meet, distinguishing exact equivalences from special cases and analogies. We develop the information-theoretic foundation from first principles. We then introduce CCA and its extension to several views, followed by the engineering thread from blind multiuser detection to CCA as a blind receiver. Next, we turn to nonlinear CCA, covering kernel, deep, and probabilistic formulations, the conditions under which the common factor is identifiable, and connections to self-supervised learning. We conclude with a discussion of further extensions and open directions. The exposition is self-contained, so that readers from each of these communities can follow the parts developed by the others.

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Topics: Canonical correlation, Probabilistic logic, Computer science, Artificial intelligence, Bridging (networking), Subspace topology

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