Margin Maximization as Lossless Maximal Compression
This work addresses the fundamental problem of understanding generalization in supervised machine learning for researchers and practitioners, offering a novel theoretical framework that is incremental but insightful.
The paper provides an information-theoretic interpretation of margin maximization in classification as achieving lossless maximal compression of noiseless training data, extracting all useful information for predicting labels without excess. It offers new insights on generalization, explaining the success and limitations of algorithms like gradient boosting through theoretical arguments and empirical evidence.
The ultimate goal of a supervised learning algorithm is to produce models constructed on the training data that can generalize well to new examples. In classification, functional margin maximization -- correctly classifying as many training examples as possible with maximal confidence --has been known to construct models with good generalization guarantees. This work gives an information-theoretic interpretation of a margin maximizing model on a noiseless training dataset as one that achieves lossless maximal compression of said dataset -- i.e. extracts from the features all the useful information for predicting the label and no more. The connection offers new insights on generalization in supervised machine learning, showing margin maximization as a special case (that of classification) of a more general principle and explains the success and potential limitations of popular learning algorithms like gradient boosting. We support our observations with theoretical arguments and empirical evidence and identify interesting directions for future work.