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Pacific Rim International Conference on Artificial Intelligence

PRICAI 2016: Trends in Artificial Intelligence: PRICAI 2016 Workshops pp 26–37Cite as

A Regression-Based SVD Parallelization Using Overlapping Folds for Textual Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10004))

Abstract

One of the most difficult issues in text mining is high dimensionality caused by a large number of features (keywords). While various multivariate analyses, such as PCA and SVD (in information retrieval, called LSI), are developed to solve this curse of high dimensionality, they are computationally costly. This paper investigates a regression-based reconstruction method that enables parallelization of PCA/SVD by decomposing a document-term matrix into a set of sub-matrices with consideration of overlapped terms, and then to re-assemble using regression technique. To evaluate our method, we utilize two text datasets in the UCI Machine Learning Repository, called “Bag of Words” and “Reuter 50 50”. To measure the closeness between two documents, cosine similarity is applied while the accuracy is measured in the form of rank order mismatch. Finally, the result shows that, the matrices decomposition and re-assembly can preserve the quality of relation/representation.

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Acknowledgement

This work is financially funded and supported by Sirindhorn International Institute of Technology, Thammasat University and Burapha University.

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Authors and Affiliations

  1. School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Bangkok, Pathum Thani, Thailand

    Uraiwan Buatoom, Thanaruk Theeramunkong & Waree Kongprawechnon

  2. Associate Fellow, The Royal Society of Thailand, Bangkok, Thailand

    Thanaruk Theeramunkong

  3. Faculty of Science and Arts, Burapha University, Chanthaburi, Thailand

    Uraiwan Buatoom

Authors
  1. Uraiwan Buatoom
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  2. Thanaruk Theeramunkong
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  3. Waree Kongprawechnon
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Corresponding author

Correspondence to Uraiwan Buatoom .

Editor information

Editors and Affiliations

  1. ISIR, Osaka University, Ibaraki, Osaka, Japan

    Masayuki Numao

  2. Thammasat University, Bangkadi Muang Pathumthani, Thailand

    Thanaruk Theeramunkong

  3. National Electronics and Computer Technology Center, Language and Semantic Technology Lab, Pathum Thani, Thailand

    Thepchai Supnithi

  4. Information Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

    Mahasak Ketcham

  5. Mahidol University, Bangkok, Thailand

    Narit Hnoohom

  6. Media Technology, King Mongkut's University of Technology Thonburi, Thonburi, Thailand

    Patiyuth Pramkeaw

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Buatoom, U., Theeramunkong, T., Kongprawechnon, W. (2017). A Regression-Based SVD Parallelization Using Overlapping Folds for Textual Data. In: Numao, M., Theeramunkong, T., Supnithi, T., Ketcham, M., Hnoohom, N., Pramkeaw, P. (eds) Trends in Artificial Intelligence: PRICAI 2016 Workshops. PRICAI 2016. Lecture Notes in Computer Science(), vol 10004. Springer, Cham. https://doi.org/10.1007/978-3-319-60675-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-60675-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60674-3

  • Online ISBN: 978-3-319-60675-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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