Subspace Mapping of Noisy Text Documents

  • Axel J. Soto
  • Marc Strickert
  • Gustavo E. Vazquez
  • Evangelos Milios
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6657)


Subspace mapping methods aim at projecting high-dimensional data into a subspace where a specific objective function is optimized. Such dimension reduction allows the removal of collinear and irrelevant variables for creating informative visualizations and task-related data spaces. These specific and generally de-noised subspaces spaces enable machine learning methods to work more efficiently. We present a new and general subspace mapping method, Correlative Matrix Mapping (CMM), and evaluate its abilities for category-driven text organization by assessing neighborhood preservation, class coherence, and classification. This approach is evaluated for the challenging task of processing short and noisy documents.


Subspace Mapping Compressed Document Representation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Axel J. Soto
    • 1
  • Marc Strickert
    • 2
  • Gustavo E. Vazquez
    • 3
  • Evangelos Milios
    • 1
  1. 1.Faculty of Computer ScienceDalhousie UniversityCanada
  2. 2.Institute for Vision and GraphicsSiegen UniversityGermany
  3. 3.Dept. Computer ScienceUniv. Nacional del SurArgentina

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