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Encyclopedia of Database Systems pp 1108–1115Cite as

Dimensionality Reduction Techniques for Nearest-Neighbor Computations

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Synonyms

Clustering; Karhunen-Loève transform (KLT); Multi-dimensional indexing; Nearest neighbors query; Principal component analysis (PCA); Singular value decomposition (SVD)

Definition

Representing objects such as images by their feature vectors and searching for similarity according to the distances of the points representing them in high-dimensional space via k-nearest-neighbor (k-NN) queries to a target image are a popular paradigm. Dimensionality reduction via singular value decomposition (SVD) to individual clusters of a dataset results in higher dimensionality reduction for the same normalized mean square error (NMSE) than applying singular value decomposition (SVD) to the whole dataset. The cost of processing k-NN queries is further reduced by suitable indexing structures such as the ordered partition (OP)-tree and the stepwise dimensionality increasing (SDI)-tree.

Historical Background

IBM’s Query by Image Content (QBIC) project, which utilized content-based image retrieval...

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Authors and Affiliations

  1. Thomasian and Associates, Pleasantville, NY, USA

    Alexander Thomasian

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  1. Alexander Thomasian
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Correspondence to Alexander Thomasian .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

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Thomasian, A. (2018). Dimensionality Reduction Techniques for Nearest-Neighbor Computations. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_80771

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