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Local Deep-Feature Alignment for Unsupervised Dimension Reduction

delete2018-05-01
delete182
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OA
AI
张
张剑 (Jian Zhang)
J
Jun Yu *
D
Dacheng Tao
DOI:10.1109/TIP.2018.2804218delete
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摘要

摘要

En 中文
This paper presents an unsupervised deep-learning framework named local deep-feature alignment (LDFA) for dimension reduction. We construct neighbourhood for each data sample and learn a local stacked contractive autoencoder (SCAE) from the neighbourhood to extract the local deep features. Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features. Moreover, we derive an approach from LDFA to map explicitly a new data sample into the learned low-dimensional subspace. The advantage of the LDFA method is that it learns both local and global characteristics of the data sample set: the local SCAEs capture local characteristics contained in the data set, while the global alignment procedures encode the interdependencies between neighbourhoods into the final low-dimensional feature representations. Experimental results on data visualization, clustering, and classification show that the LDFA method is competitive with several well-known dimension reduction techniques, and exploiting locality in deep learning is a research topic worth further exploring.
Keyword:
Deep learning
auto-encoder
locality preserving
global alignment
dimension reduction
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期刊

IEEE Transactions on Image Processing 封面图
IEEE Transactions on Image Processing
IF:
13.7
论文数:
1.0W
被引数:
8.4W

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Z
zhejiang international studies university
学者数:
192
论文数: 206
被引数: 3
H
Hangzhou Dianzi University
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1.3W
论文数: 9.6K
被引数: 7.5K
U
University of Sydney
学者数:
6.5W
论文数: 6.2W
被引数: 90
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