Manifold learning regression with non-stationary kernels

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    5 Citations (Scopus)


    Nonlinear multi-output regression problem is to construct a predictive function which estimates an unknown smooth mapping from q-dimensional inputs to m-dimensional outputs based on a training data set consisting of given “input-output” pairs. In order to solve this problem, regression models based on stationary kernels are often used. However, such approaches are not efficient for functions with strongly varying gradients. There exist some attempts to introduce non-stationary kernels to account for possible non-regularities, although even the most efficient one called Manifold Learning Regression (MLR), which estimates the unknown function as well its Jacobian matrix, is too computationally expensive. The main problem is that the MLR is based on a computationally intensive manifold learning technique. In this paper we propose a modified version of the MLR with significantly less computational complexity while preserving its accuracy.

    Original languageEnglish
    Title of host publicationArtificial Neural Networks in Pattern Recognition - 8th IAPR TC3 Workshop, ANNPR 2018, Proceedings
    EditorsLuca Pancioni, Edmondo Trentin, Friedhelm Schwenker
    PublisherSpringer Verlag
    Number of pages13
    ISBN (Print)9783319999777
    Publication statusPublished - 2018
    Event8th IAPR TC3 workshop on Artificial Neural Networks for Pattern Recognition, ANNPR 2018 - Siena, Italy
    Duration: 19 Sep 201821 Sep 2018

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11081 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference8th IAPR TC3 workshop on Artificial Neural Networks for Pattern Recognition, ANNPR 2018


    • Manifold learning regression
    • Non-stationary kernel
    • Nonlinear multi-output regression


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