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

• URL: http://arxiv.org/abs/2004.12209v3
• Date: Sat, 25 Jul 2020 17:06:53 GMT
• Title: Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
• Authors: Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang
• Abstract summary: In this work we develop an algorithm for a variety of generalized representations in a semi-norms that representers in a lead, and bounds are established. This allows in representations to be learned efficiently and effectively as confirmed in our experiments along with accurate predictions.
• Score: 32.442549424823355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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.

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