Manifold learning regression with non-stationary kernels

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    5 Цитирования (Scopus)

    Аннотация

    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.

    Язык оригиналаАнглийский
    Название основной публикацииArtificial Neural Networks in Pattern Recognition - 8th IAPR TC3 Workshop, ANNPR 2018, Proceedings
    РедакторыLuca Pancioni, Edmondo Trentin, Friedhelm Schwenker
    ИздательSpringer Verlag
    Страницы152-164
    Число страниц13
    ISBN (печатное издание)9783319999777
    DOI
    СостояниеОпубликовано - 2018
    Событие8th IAPR TC3 workshop on Artificial Neural Networks for Pattern Recognition, ANNPR 2018 - Siena, Италия
    Продолжительность: 19 сент. 201821 сент. 2018

    Серия публикаций

    НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Том11081 LNAI
    ISSN (печатное издание)0302-9743
    ISSN (электронное издание)1611-3349

    Конференция

    Конференция8th IAPR TC3 workshop on Artificial Neural Networks for Pattern Recognition, ANNPR 2018
    Страна/TерриторияИталия
    ГородSiena
    Период19/09/1821/09/18

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