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Proceedings Paper

Parallel implementation of nonlinear dimensionality reduction methods applied in object segmentation using CUDA in GPU
Author(s): Romel Campana-Olivo; Vidya Manian
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Paper Abstract

Manifold learning, also called nonlinear dimensionality reduction, affords a way to understand and visualize the structure of nonlinear hyperspectral datasets. These methods use graphs to represent the manifold topology, and use metrics like geodesic distance, allowing embedding higher dimension objects into lower dimension. However the complexities of some manifold learning algorithms are O(N3), therefore they are very slow (high computational algorithms). In this paper we present a CUDA-based parallel implementation of the three most popular manifold learning algorithms like Isomap, Locally linear embedding, and Laplacian eigenmaps, using CUDA multi-thread model. The result of this dimensionality reduction was employed in segmentation using active contours as an application of these reduced hyperspectral images. The manifold learning algorithms were implemented on a 64-bit workstation equipped with a quad-core Intel® Xeon with 12 GB RAM and two NVIDIA Tesla C1060 GPU cards. Manifold learning outperforms significantly and achieve up to 26x speedup. It also shows good scalability where varying the size of the dataset and the number of K nearest neighbors.

Paper Details

Date Published: 20 May 2011
PDF: 12 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80480R (20 May 2011); doi: 10.1117/12.884767
Show Author Affiliations
Romel Campana-Olivo, Univ. de Puerto Rico (United States)
Vidya Manian, Univ. de Puerto Rico (United States)


Published in SPIE Proceedings Vol. 8048:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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