教師資料查詢 | 類別: 期刊論文 | 教師: 衛信文 Wei, Hsin-Wen (瀏覽個人網頁)

標題:Parallel non-linear dimension reduction algorithm on GPU
學年99
學期1
出版(發表)日期2011/01/01
作品名稱Parallel non-linear dimension reduction algorithm on GPU
作品名稱(其他語言)
著者Yeh, Tsung-Tai; Chen, Tseng-Yi; Chen, Yen-Chiu; Wei, Hsin-Wen
單位淡江大學電機工程學系
出版者Inderscience Publishers
著錄名稱、卷期、頁數International Journal of Granular Computing, Rough Sets and Intelligent Systems 2(2), pp.149-165
摘要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.
關鍵字nonlinear dimension reduction; dimensionality reduction; GPU; complex datasets; memory space; graphics processing unit
語言英文
ISSN1757-2703
期刊性質國外
收錄於
產學合作國外
通訊作者Wei, Hsin-Wen
審稿制度
國別英國
公開徵稿
出版型式紙本
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