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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/95752

    Title: Parallel non-linear dimension reduction algorithm on GPU
    Authors: Yeh, Tsung-Tai;Chen, Tseng-Yi;Chen, Yen-Chiu;Wei, Hsin-Wen
    Contributors: 淡江大學電機工程學系
    Keywords: nonlinear dimension reduction;dimensionality reduction;GPU;complex datasets;memory space;graphics processing unit
    Date: 2011
    Issue Date: 2014-02-12 21:36:08 (UTC+8)
    Publisher: Inderscience Publishers
    Abstract: Advances in non-linear dimensionality reduction provide a way to understand and visualise the underlying structure of complex datasets. The performance of large-scale non-linear dimensionality reduction is of key importance in data mining, machine learning, and data analysis. In this paper, we concentrate on improving the performance of non-linear dimensionality reduction using large-scale datasets on the GPU. In particular, we focus on solving problems including k-nearest neighbour (KNN) search and sparse spectral decomposition for large-scale data, and propose an efficient framework for local linear embedding (LLE). We implement a k-d tree-based KNN algorithm and Krylov subspace method on the GPU to accelerate non-linear dimensionality reduction for large-scale data. Our results enable GPU-based k-d tree LLE processes of up to about 30-60? faster compared to the brute force KNN (Hernandez et al., 2007) LLE model on the CPU. Overall, our methods save O(n²-6n-2k-3) memory space.
    Relation: International Journal of Granular Computing, Rough Sets and Intelligent Systems 2(2), pp.149-165
    DOI: 10.1504/IJGCRSIS.2011.043370
    Appears in Collections:[資訊管理學系暨研究所] 期刊論文

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