Skip to main content

Subspace Mapping of Noisy Text Documents

  • Conference paper
Advances in Artificial Intelligence (Canadian AI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6657))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Zhang, J., Huang, H., Wang, J.: Manifold Learning for Visualizing and Analyzing High-Dimensional Data. IEEE Intel. Syst. 25, 54–61 (2010)

    Article  Google Scholar 

  2. van der Maaten, L., Postma, E., van den Herik, J.: Dimensionality Reduction: A Comparative Review. Tilburg University, TiCC TR 2009–005 (2009)

    Google Scholar 

  3. Strickert, M., Soto, A.J., Vazquez, G.E.: Adaptive Matrix Distances Aiming at Optimum Regression Subspaces. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN 2010, pp. 93–98 (2010)

    Google Scholar 

  4. Soto, A.J., Strickert, M., Vazquez, G.E., Milios, E.: Adaptive Visualization of Text Documents Incorporating Domain Knowledge. In: Challenges of Data Visualization, NIPS 2010 Workshop (2010)

    Google Scholar 

  5. Machine Learning Open Source Software, http://mloss.org

  6. Matlab Statistics Toolbox, http://www.mathworks.com/products/statistics/

  7. McLachlan, G.: Discriminant Analysis and Statistical Pattern Recognition. Wiley-Interscience, Hoboken (2004)

    MATH  Google Scholar 

  8. Hardoon, D.R., Szedmak, S.R., Shawe-Taylor, J.R.: Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Comput. 16, 2639–2664 (2004)

    Article  MATH  Google Scholar 

  9. Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighborhood Components Analysis. Adv. Neural Inf. Process. Syst. 17, 513–520 (2005)

    Google Scholar 

  10. Globerson, A., Roweis, S.: Metric Learning by Collapsing Classes. Adv. Neural Inf. Process. Syst. 18, 451–458 (2006)

    Google Scholar 

  11. Aviation Safety Reporting System, http://asrs.arc.nasa.gov/

  12. Lee, J.A., Verleysen, M.: Quality Assessment of Dimensionality Reduction: Rank-Based Criteria. Neurocomputing 72, 1431–1443 (2009)

    Article  Google Scholar 

  13. Dunnet, C.W.: A Multiple Comparisons Procedure for Comparing Several Treatments with a Control. J. Am. Stat. Assoc. 50, 1096–1121 (1955)

    Article  Google Scholar 

  14. Soto, A.J., Strickert, M., Vazquez, G.E., Milios, E.: Technical Report, Dalhousie University (in preparation), http://www.cs.dal.ca/research/techreports

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Soto, A.J., Strickert, M., Vazquez, G.E., Milios, E. (2011). Subspace Mapping of Noisy Text Documents. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21043-3_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21042-6

  • Online ISBN: 978-3-642-21043-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics