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Sparse Principal Component Analysis for Natural Language Processing

Author

Listed:
  • Reza Drikvandi

    (Manchester Metropolitan University)

  • Olamide Lawal

    (Manchester Metropolitan University)

Abstract

High dimensional data are rapidly growing in many different disciplines, particularly in natural language processing. The analysis of natural language processing requires working with high dimensional matrices of word embeddings obtained from text data. Those matrices are often sparse in the sense that they contain many zero elements. Sparse principal component analysis is an advanced mathematical tool for the analysis of high dimensional data. In this paper, we study and apply the sparse principal component analysis for natural language processing, which can effectively handle large sparse matrices. We study several formulations for sparse principal component analysis, together with algorithms for implementing those formulations. Our work is motivated and illustrated by a real text dataset. We find that the sparse principal component analysis performs as good as the ordinary principal component analysis in terms of accuracy and precision, while it shows two major advantages: faster calculations and easier interpretation of the principal components. These advantages are very helpful especially in big data situations.

Suggested Citation

  • Reza Drikvandi & Olamide Lawal, 2023. "Sparse Principal Component Analysis for Natural Language Processing," Annals of Data Science, Springer, vol. 10(1), pages 25-41, February.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:1:d:10.1007_s40745-020-00277-x
    DOI: 10.1007/s40745-020-00277-x
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    References listed on IDEAS

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    1. Reza Drikvandi & Ahmad Khodadadi & Geert Verbeke, 2012. "Testing variance components in balanced linear growth curve models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 563-572, July.
    2. Journée, Michel & Nesterov, Yurii & Richtarik, Peter & Sepulchre, Rodolphe, 2008. "Generalized power method for sparse principal component analysis," LIDAM Discussion Papers CORE 2008070, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Tanin Sirimongkolkasem & Reza Drikvandi, 2019. "On Regularisation Methods for Analysis of High Dimensional Data," Annals of Data Science, Springer, vol. 6(4), pages 737-763, December.
    4. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
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