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Subspace Mapping of Noisy Text Documents

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Advances in Artificial Intelligence (Canadian AI 2011)

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

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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.

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© 2011 Springer-Verlag Berlin Heidelberg

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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.

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  • 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)

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