Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang


<p>Invariance (defined in a general sense) has been one of the most effective priors for representation learning. Direct factorization of parametric models is feasible only for a small range of invariances, while regularization approaches, despite improved generality, lead to nonconvex optimization. In this work, we develop a \emph{convex} representation learning algorithm for a variety of generalized invariances that can be modeled as semi-norms. Novel Euclidean embeddings are introduced for kernel representers in a semi-inner-product space, and approximation bounds are established. This allows invariant representations to be learned efficiently and effectively as confirmed in our experiments, along with accurate predictions.</p>