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  • title: Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
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            Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
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            Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption

            Jul 24, 2023

            Speakers

            VF

            Vasilii Feofanov

            Speaker · 0 followers

            MT

            Malik Tiomoko

            Speaker · 0 followers

            AV

            Aladin Virmaux

            Speaker · 0 followers

            About

            We propose a theoretical framework to analyze semi-supervised classification under the low density separation assumption in a high-dimensional regime. In particular, we introduce QLDS, a linear classification model, where the low density separation assumption is implemented via quadratic margin maximization. The algorithm has an explicit solution with rich theoretical properties, and we show that particular cases of our algorithm are the least-square support vector machine in the supervised case…

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            ICML 2023

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