Abstract
Recently, an extension of popular learning vector quantization (LVQ) to general dissimilarity data has been proposed, relational generalized LVQ (RGLVQ) [10,9]. An intuitive prototype based classification scheme results which can divide data characterized by pairwise dissimilarities into priorly given categories. However, the technique relies on the full dissimilarity matrix and, thus, has squared time complexity and linear space complexity. In this contribution, we propose an intuitive linear time and constant space approximation of RGLVQ by means of patch processing. An efficient heuristic which maintains the good classification accuracy and interpretability of RGLVQ results, as demonstrated in three examples from the biomdical domain.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alex, N., Hasenfuss, A., Hammer, B.: Patch clustering for massive data sets. Neurocomputing 72(7-9), 1455–1469 (2009)
Arlt, W., et al.: Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors. Journal of Clinical Endocrinology and Metabolism 96(12), 3775–3784 (2011)
Barbuddhe, S.B., Maier, T., Schwarz, G., Kostrzewa, M., Hof, H., Domann, E., Chakraborty, T., Hain, T.: Rapid identification and typing of listeria species by matrix-assisted laser desorption ionization-time of flight mass spectrometry. Applied and Environmental Microbiology 74(17), 5402–5407 (2008)
Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.-C., Estreicher, A., Gasteiger, E., Martin, M., Michoud, K., O’Donovan, C., Phan, I., Pilbout, S., Schneider, M.: The swiss-prot protein knowledge base and its supplement trembl in 2003. Nucleic Acids Research 31, 365–370 (2003)
Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R., Bairoch, A.: Expasy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31, 3784–3788 (2003)
Gusfield, D.: Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology. Cambridge University Press (1997)
Hammer, B.: Learning with Recurrent Neural Networks. LNCIS, vol. 254. Springer (2000)
Hammer, B., Hasenfuss, A.: Topographic mapping of large dissimilarity datasets. Neural Computation 22(9), 2229–2284 (2010)
Hammer, B., Mokbel, B., Schleif, F.-M., Zhu, X.: Prototype-Based Classification of Dissimilarity Data. In: Gama, J., Bradley, E., Hollmén, J. (eds.) IDA 2011. LNCS, vol. 7014, pp. 185–197. Springer, Heidelberg (2011)
Hammer, B., Mokbel, B., Schleif, F.-M., Zhu, X.: White Box Classification of Dissimilarity Data. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part III. LNCS, vol. 7208, pp. 309–321. Springer, Heidelberg (2012)
Kohonen, T. (ed.): Self-Organizing Maps, 3rd edn. Springer-Verlag New York, Inc. (2001)
Kohonen, T., Somervuo, P.: How to make large self-organizing maps for nonvectorial data. Neural Networks 15, 945–952 (2002)
Lundsteen, C., Phillip, J., Granum, E.: Quantitative analysis of 6985 digitized trypsin g-banded human metaphase chromosomes. Clinical Genetics 18, 355–370 (1980)
Maier, T., Klebel, S., Renner, U., Kostrzewa, M.: Fast and reliable maldi-tof ms–based microorganism identification. Nature Methods (3) (2006)
Neuhaus, M., Bunke, H.: Edit distance based kernel functions for structural pattern classification. Pattern Recognition 39(10), 1852–1863 (2006)
Schleif, F.-M., Hammer, B., Kostrzewa, M., Villmann, T.: Exploration of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics 9(2), 129–143 (2008)
Schneider, P., Biehl, M., Hammer, B.: Adaptive relevance matrices in learning vector quantization. Neural Computation 21(12), 3532–3561 (2009)
Seo, S., Obermayer, K.: Soft learning vector quantization. Neural Computation 15, 1589–1604 (2002)
Williams, C., Seeger, M.: Using the Nyström method to speed up kernel machines. In: Advances in Neural Information Processing Systems 13, pp. 682–688. MIT Press (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, X., Schleif, FM., Hammer, B. (2012). Patch Processing for Relational Learning Vector Quantization. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-31346-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31345-5
Online ISBN: 978-3-642-31346-2
eBook Packages: Computer ScienceComputer Science (R0)