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2018 24th International Conference on Pattern Recognition (ICPR) (2018)
Beijing, China
Aug. 20, 2018 to Aug. 24, 2018
ISSN: 1051-4651
ISBN: 978-1-5386-3789-0
pp: 3186-3191
Zhiling Ye , Xiamen University, Xiamen, China
Zhihong Zhang , Xiamen University, Xiamen, China
Lu Bai , Central University of Finance and Economics, Beijing, China
Guosheng Hu , Anyvision company, Belfast, UK
Zheng-Jian Bai , Xiamen University, Xiamen, China
Yiqun Hu , Xiamen University, Xiamen, China
Edwin R. Hancock , Department of Computer Science, University of York, York, UK
ABSTRACT
In this work we propose a novel and compact Neighbor Reconstruction Method (NRM) which is a unified pre-processing method for graph-based sparse spectral algorithms. This method is conducted by vector operations on a central point and its corresponding neighbor points. NRM generates new neighbor points which can capture the local space structure of the central point more appropriately than original neighbor points. With NRM, a large number of sparse spectral based nonlinear feature extraction and selection algorithms gain significant improvement. Specifically, we embedded NRM to several classical algorithms, Local Linear Embedding (LLE) [1], Laplacian Eigenmaps (LE) [2] and Unsupervised Feature Selection for Multi-cluster Data (MCFS) [3], with accuracy improvement of up to 7%, 2.6%, 2.4% on ORL, CIFAR 10, and MINST data sets respectively. We also apply NRM to a Super Resolution algorithm, A+ [5], and obtain 0.12dB improvement than original method.
INDEX TERMS
Feature extraction, Manifolds, Sparse matrices, Reconstruction algorithms, Dimensionality reduction, Measurement, Computational modeling
CITATION

Z. Ye et al., "A Unified Neighbor Reconstruction Method for Embeddings," 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, 2018, pp. 3186-3191.
doi:10.1109/ICPR.2018.8545248
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