Abstract
The goal of this work is to improve visualizations by using a task-related metric in dimension reduction. In supervised setting, metric can be learned directly from data or extracted from a model fitted to data. Here, two model-based approaches are tried: extracting a global metric from classifier parameters, and doing dimension reduction in feature space of a classifier. Both approaches are tested using four dimension reduction methods and four real data sets. Both approaches are found to improve visualization results. Especially working in classifier feature space is beneficial for showing possible cluster structure of the data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rasmussen, C.E., Williams, C.K.I.: Gaussian processes for machine learning. MIT Press, Cambridge (2006)
Lampinen, J., Vehtari, A.: Bayesian approach for neural networks – review and case studies. Neural Networks 14(3), 7–24 (2001)
Ye, J., Zhao, Z., Liu, H.: Adaptive distance metric learning for clustering. In: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007)
Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.: Distance metric learning, with application to clustering with side-information. In: NIPS 15, pp. 505–512 (2002)
Weinberger, K.Q., Sha, F., Saul, L.K.: Learning a kernel matrix for nonlinear dimensionality reduction. In: Proc. 21st International Conference on Machine Learning, pp. 839–846 (2004)
Strickert, M., Schneider, P., Keilwagen, J., Villmann, T., Biehl, M., Hammer, B.H.: Discriminatory data mapping by matrix-based supervised learning metrics. In: Prevost, L., Marinai, S., Schwenker, F. (eds.) ANNPR 2008. LNCS (LNAI), vol. 5064, pp. 78–89. Springer, Heidelberg (2008)
Globerson, A., Roweis, S.: Metric learning by collapsing classes. In: NIPS 18, pp. 451–458 (2005)
Lanckriet, G.R.G., Cristianini, N., Bartlett, P., El Ghaoui, L., Jordan, M.I.: Learning the kernel matrix with semidefinite programming. JMLR 5, 27–72 (2004)
Jaakkola, T.S., Haussler, D.: Exploiting generative models in discriminative classifiers. In: NIPS 11 (1998)
Seeger, M.: Covariance kernels from Bayesian generative models. In: NIPS 14, pp. 905–912 (2002)
Peltonen, J., Klami, A., Kaski, S.: Learning more accurate metrics for self-organizing maps. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 999–1004. Springer, Heidelberg (2002)
Williams, C.K.I.: Computation with infinite neural networks. Neural Computation 10, 1203–1216 (1998)
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. JMLR 9, 2579–2605 (2008)
Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers C-18(5), 401–409 (1969)
Tenenbaum, J.B., de Silva, V., Langford: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2322 (2000)
Schölkopf, B., Smola, A., Müller, K.: Kernel principal components analysis. In: Advances in kernel methods: support vector learning, pp. 327–352. MIT Press, Cambridge (1999)
Sund, R.: Methodological perspectives for register-based health system performance assessment. Developing a hip fracture monitoring system in Finland. Technical Report Stakes Research Report 174, National Research and Development Centre for Welfare and Health, Helsinki, Finland (2008)
Sund, R., Riihimäki, J., Mäkelä, M., Vehtari, A., Lüthje, P., Huusko, T., Häkkinen, U.: Modeling the length of the care episode after hip fracture: does the type of fracture matter? Scandinavian Journal of Surgery (in press, 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Parviainen, E., Vehtari, A. (2009). Features and Metric from a Classifier Improve Visualizations with Dimension Reduction. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-04277-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
Online ISBN: 978-3-642-04277-5
eBook Packages: Computer ScienceComputer Science (R0)