Limited Rank Matrix Learning, discriminative dimension reduction and visualization

Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M (2012)
Neural Networks 26: 159-173.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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Abstract / Bemerkung
We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently. In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two- or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method. (c) 2011 Elsevier Ltd. All rights reserved.
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Zeitschriftentitel
Neural Networks
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26
Seite
159-173
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Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M. Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks. 2012;26:159-173.
Bunte, K., Schneider, P., Hammer, B., Schleif, F. - M., Villmann, T., & Biehl, M. (2012). Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks, 26, 159-173. doi:10.1016/j.neunet.2011.10.001
Bunte, K., Schneider, P., Hammer, B., Schleif, F. - M., Villmann, T., and Biehl, M. (2012). Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks 26, 159-173.
Bunte, K., et al., 2012. Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks, 26, p 159-173.
K. Bunte, et al., “Limited Rank Matrix Learning, discriminative dimension reduction and visualization”, Neural Networks, vol. 26, 2012, pp. 159-173.
Bunte, K., Schneider, P., Hammer, B., Schleif, F.-M., Villmann, T., Biehl, M.: Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks. 26, 159-173 (2012).
Bunte, Kerstin, Schneider, Petra, Hammer, Barbara, Schleif, Frank-Michael, Villmann, Thomas, and Biehl, Michael. “Limited Rank Matrix Learning, discriminative dimension reduction and visualization”. Neural Networks 26 (2012): 159-173.

6 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Prototype-based models in machine learning.
Biehl M, Hammer B, Villmann T., Wiley Interdiscip Rev Cogn Sci 7(2), 2016
PMID: 26800334
Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge.
Biehl M, Sadowski P, Bhanot G, Bilal E, Dayarian A, Meyer P, Norel R, Rhrissorrakrai K, Zeller MD, Hormoz S., Bioinformatics 31(4), 2015
PMID: 24994890
Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology.
Nagarajan MB, Huber MB, Schlossbauer T, Leinsinger G, Krol A, Wismüller A., Artif Intell Med 60(1), 2014
PMID: 24355697
Promoting cold-start items in recommender systems.
Liu JH, Zhou T, Zhang ZK, Yang Z, Liu C, Li WM., PLoS One 9(12), 2014
PMID: 25479013
Analysis of flow cytometry data by matrix relevance learning vector quantization.
Biehl M, Bunte K, Schneider P., PLoS One 8(3), 2013
PMID: 23527184

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