Computer Science ›› 2015, Vol. 42 ›› Issue (11): 90-93.doi: 10.11896/j.issn.1002-137X.2015.11.018

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Hypergraph Dimensionality Reduction with Multiple Feature Fusion Based on GPU Parallel Acceleration

HONG Chao-qun, CHEN Xu-hui, WANG Xiao-dong, LI Shi-jin and WU Ke-shou   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Graph-based learning methods are currently popular for dimensionality reduction.However,for multiple feature data,different relationships from different features are hard to be integrated into a single graph.In this paper,a novel semi-supervised dimensionality reduction method was proposed for multiple feature data.First,the hyperedges in hypergraph are assumed as patches.In this way,hypergraph is applied to patch alignment framework.Then,the weights of hyperedges are computed with statistics of distances between neighboring pairs and the patches from different features are integrated.Second,the speed of computing Euclidean distances and matrix multiplication is improved by using GPU,since they take most of time in constructing the Laplacian matrix.The experimental results demonstrate the improvement on both classification performance and learning speed.

Key words: Dimensionality reduction,Multiple feature fusion,Patch alignment framework,Hypergraph learning,GPU-based parallel acceleration

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