Nonlinear multi-output regression on unknown input manifold

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Abstract

Consider unknown smooth function which maps high-dimensional inputs to multidimensional outputs and whose domain of definition is unknown low-dimensional input manifold embedded in an ambient high-dimensional input space. Given training dataset consisting of ‘input-output’ pairs, regression on input manifold problem is to estimate the unknown function and its Jacobian matrix, as well to estimate the input manifold. By transforming high-dimensional inputs in their low-dimensional features, initial regression problem is reduced to certain regression on feature space problem. The paper presents a new geometrically motivated method for solving both interrelated regression problems.

Keywords

Manifold learning Regression on manifolds Regression on feature space Manifold estimation Dimensionality reduction 

Mathematics Subject Classification (2010)

62J02 

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© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Skolkovo Institute of Science and TechnologyMoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia

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