Growing Curvilinear Component Analysis (GCCA) for Dimensionality Reduction of Nonstationary Data-Reference-Cited by-同舟云学术

Growing Curvilinear Component Analysis (GCCA) for Dimensionality Reduction of Nonstationary Data

Published:2017-08-30 Issue: Volume: Page:151-160
ISSN:2190-3018
Container-title:Multidisciplinary Approaches to Neural Computing
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Author:

Cirrincione Giansalvo,Randazzo Vincenzo,Pasero Eros

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-56904-8_15

Reference16 articles.

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3. Weng, J., Zhang, Y., Hwang, W.S.: Candid covariance-free incremental principal components analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 1034–1040 (2003)

4. Kouropteva, O., Okun, O., Pietikainen, M.: Incremental locally linear embedding. Pattern Recogn. 38, 1764–1767 (2005)

5. De Ridder, D., Duin, R.: Sammon’s mapping using neural networks: a comparison. Pattern Recogn. Lett. 18, 1307–1316 (1997)

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