Incremental construction of low-dimensional data representations

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    7 Citations (Scopus)


    Various Dimensionality Reduction algorithms transform initial high-dimensional data into their lower-dimensional representations preserving chosen properties of the initial data. Typically, such algorithms use the solution of large-dimensional optimization problems, and the incremental versions are designed for many popular algorithms to reduce their computational complexity. Under manifold assumption about high-dimensional data, advanced manifold learning algorithms should preserve the Datamanifold and its differential properties such as tangent spaces, Riemannian tensor, etc. Incremental version of the Grassmann&Stiefel Eigenmaps manifold learning algorithm, which has asymptotically minimal reconstruction error, is proposed in this paper and has significantly smaller computational complexity in contrast to the initial algorithm.

    Original languageEnglish
    Title of host publicationArtificial Neural Networks in Pattern Recognition - 7th IAPR TC3 Workshop, ANNPR 2016, Proceedings
    EditorsFriedhelm Schwenker, Hazem M. Abbas, Neamat El Gayar, Edmondo Trentin
    PublisherSpringer Verlag
    Number of pages13
    ISBN (Print)9783319461816
    Publication statusPublished - 2016
    Event7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016 - Ulm, Germany
    Duration: 28 Sep 201630 Sep 2016

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9896 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016


    • Dimensionality reduction
    • Incremental learning
    • Machine learning
    • Manifold learning
    • Tangent bundle manifold learning


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