Augmented embedding of dissimilarity data into (pseudo-) Euclidean spaces

Elzbieta Pekalska, Artsiom Harol, Elzbieta Pȩkalska, Sergey Verzakov, Robert P W Duin

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Pairwise proximities describe the properties of objects in terms of their similarities. By using different distance-based functions one may encode different characteristics of a given problem. However, to use the framework of statistical pattern recognition some vector representation should be constructed. One of the simplest ways to do that is to define an isometric embedding to some vector space. In this work, we will focus on a linear embedding into a (pseudo-) Euclidean space. This is usually well defined for training data. Some inadequacy, however, appears when projecting new or test objects due to the resulting projection errors. In this paper we propose an augmented embedding algorithm that enlarges the dimensionality of the space such that the resulting projection error vanishes. Our preliminary results show that it may lead to a better classification accuracy, especially for data with high intrinsic dimensionality. © Springer-Verlag Berlin Heidelberg 2006.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Pages613-621
    Number of pages8
    Volume4109
    ISBN (Print)3540372369, 9783540372363
    DOIs
    Publication statusPublished - 2006
    EventJoint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, SSPR 2006 and SPR 2006 - Hong Kong
    Duration: 1 Jul 2006 → …
    http://dblp.uni-trier.de/db/conf/sspr/sspr2006.html#DuinP06http://dblp.uni-trier.de/rec/bibtex/conf/sspr/DuinP06.xmlhttp://dblp.uni-trier.de/rec/bibtex/conf/sspr/DuinP06

    Publication series

    NameLecture Notes in Computer Science

    Conference

    ConferenceJoint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, SSPR 2006 and SPR 2006
    CityHong Kong
    Period1/07/06 → …
    Internet address

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