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Fast Similarity Computation in Factorized Tensors

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7404))

Abstract

Low-rank factorizations of higher-order tensors have become an invaluable tool for researchers from many scientific disciplines. Tensor factorizations have been successfully applied for moderately sized multimodal data sets involving a small number of modes. However, a significant hindrance to the full realization of the potential of tensor methods is a lack of scalability on the client side: even when low-rank representations are provided by an external agent possessing the necessary computational resources, client applications are quickly rendered infeasible by the space requirements for explicitly storing a (dense) low-rank representation of the input tensor. We consider the problem of efficiently computing common similarity measures between entities expressed by fibers (vectors) or slices (matrices) within a given factorized tensor. We show that after appropriate preprocessing, inner products can be efficiently computed independently of the dimensions of the input tensor.

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Houle, M.E., Kashima, H., Nett, M. (2012). Fast Similarity Computation in Factorized Tensors. In: Navarro, G., Pestov, V. (eds) Similarity Search and Applications. SISAP 2012. Lecture Notes in Computer Science, vol 7404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32153-5_16

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  • DOI: https://doi.org/10.1007/978-3-642-32153-5_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32152-8

  • Online ISBN: 978-3-642-32153-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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