UDC 004.423, DOI: 10.2298/csis0902217K

Analysis of Unsupervised Dimensionality Reduction Techniques

Ch. Aswani Kumar1

  1. Intelligent Systems Division, School of Computing Sciences
    VIT University, Vellore-632014, India


Domains such as text, images etc contain large amounts of redundancies and ambiguities among the attributes which result in considerable noise effects (i.e. the data is high dimension). Retrieving the data from high dimensional datasets is a big challenge. Dimensionality reduction techniques have been a successful avenue for automatically extracting the latent concepts by removing the noise and reducing the complexity in processing the high dimensional data. In this paper we conduct a systematic study on comparing the unsupervised dimensionality reduction techniques for text retrieval task. We analyze these techniques from the view of complexity, approximation error and retrieval quality with experiments on four testing document collections.

Key words

Dimensionality reduction, Information retrieval, Latent semantic indexing, Matrix decompositions

Digital Object Identifier (DOI)


Publication information

Volume 6, Issue 2 (December 2009)
Year of Publication: 2009
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Kumar, C. A.: Analysis of Unsupervised Dimensionality Reduction Techniques. Computer Science and Information Systems, Vol. 6, No. 2, 217-229. (2009), https://doi.org/10.2298/csis0902217K