The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The Pre-Image Problem in Kernel Methods
Abstract
In this paper, we address the problem of finding the pre-image of a feature vector in the feature space induced by a kernel. This is of central importance in some kernel applications, such as on using kernel principal component analysis (PCA) for image denoising. Unlike the traditional method which relies on nonlinear optimization, our proposed method directly finds the location of the pre-image based on distance constraints in the feature space. It is noniterative, involves only linear algebra and does not suffer from numerical instability or local minimum problems. Evaluations on performing kernel PCA and kernel clustering on the USPS data set show much improved performance.
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
On a connection between kernel PCA and metric multidimensional scaling
Williams, C. K. I. (2002) · Machine Learning · doi:10.1023/a:1012485807823 · Cited by 206
Cited by
A Review of Feature Reduction Techniques in Neuroimaging
Mwangi, B., Tian, T. S., & Soares, J. C. (2014) · Neuroinformatics · doi:10.1007/s12021-013-9204-3 · Cited by 559
Kernel Mean Embedding of Distributions: A Review and Beyond
Muandet, K., Fukumizu, K., Sriperumbudur, B., & Schölkopf, B. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000060 · Cited by 409
Kernel Entropy Component Analysis
Jenssen, R. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.100 · Cited by 304
Metadata
- Authors
- Kwok, J. T.-Y., & Tsang, I. W.-H.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2004-11
- Volume
- 15
- Issue
- 6
- Pages
- 1517-1525
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W1675973698
- Cited by
- 437
- References
- 2
- FWCI
- 23.72
- Type
- journal article
- Language
- en
- Open access
- closed
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