The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Neighborhood preserving embedding
Abstract
Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling a probability distribution that has support on or near a submanifold of Euclidean space. In this paper, we propose a novel subspace learning algorithm called neighborhood preserving embedding (NPE). Different from principal component analysis (PCA) which aims at preserving the global Euclidean structure, NPE aims at preserving the local neighborhood structure on the data manifold. Therefore, NPE is less sensitive to outliers than PCA. Also, comparing to the recently proposed manifold learning algorithms such as Isomap and locally linear embedding, NPE is defined everywhere, rather than only on the training data points. Furthermore, NPE may be conducted in the original space or in the reproducing kernel Hilbert space into which data points are mapped. This gives rise to kernel NPE. Several experiments on face database demonstrate the effectiveness of our algorithm.
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Citations
Cites
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Cited by
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
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Patch Alignment for Dimensionality Reduction
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Self-citationRobust Structured Subspace Learning for Data Representation
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Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding
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Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
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Global and Local Structure Preservation for Feature Selection
Liu, X., Wang, L., Zhang, J., Yin, J., & Liu, H. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2287275 · Cited by 256
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Metadata
- Authors
- He, X., Cai, D., Yan, S., & Zhang, H.-J.
- Venue
- Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
- Publisher
- IEEE
- Published
- 2005
- Pages
- 1208-1213 Vol. 2
- OpenAlex
- W2103250033
- Cited by
- 1,502
- References
- 15
- FWCI
- 14.59
- Type
- conference paper
- Language
- en
- Open access
- closed
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