期刊論文
| 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 | en |
| ISSN | 1757-2703 |
| 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 | 是 |
| 國別 | GBR |
| 公開徵稿 | |
| 出版型式 | 紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/95752 ) |