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Efficient Implementation of a Dimensionality Reduction Method Using a Complex Moment-Based Subspace

Published: 20 January 2021 Publication History

Abstract

Dimensionality reduction methods are widely used for processing data efficiently. Recently Imakura et al. proposed a novel dimensionality reduction method using a complex moment-based subspace. Their method can use more eigenvectors than the existing matrix trace optimization-based methods which explains its reported higher precision. However, the computational complexity is also higher than that of the existing methods, in particular for the nonlinear kernel version. To reduce the computational complexity, we propose a practical parallel implementation of the method by introducing the Nyström approximation. We evaluate the parallel performance of our implementation using the Oakforest-PACS supercomputer.

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  • (2024)Numerical Analysis for Data RelationshipAdvanced Mathematical Science for Mobility Society10.1007/978-981-99-9772-5_4(61-77)Online publication date: 14-Mar-2024

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          cover image ACM Other conferences
          HPCAsia '21: The International Conference on High Performance Computing in Asia-Pacific Region
          January 2021
          143 pages
          ISBN:9781450388429
          DOI:10.1145/3432261
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          Published: 20 January 2021

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          1. complex moment-based method
          2. dimensionality reduction
          3. parallel computing

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          • (2024)Numerical Analysis for Data RelationshipAdvanced Mathematical Science for Mobility Society10.1007/978-981-99-9772-5_4(61-77)Online publication date: 14-Mar-2024

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