Abstract
We explore generic mechanisms to introduce structural hints into the method of Unsupervised Kernel Regression (UKR) in order to learn representations of data sequences in a semi-supervised way. These new extensions are targeted at representing a dextrous manipulation task. We thus evaluate the effectiveness of the proposed mechanisms on appropriate toy data that mimic the characteristics of the aimed manipulation task and thereby provide means for a systematic evaluation.
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References
Barreto, G., Araújo, A., Ritter, H.: Self-Organizing Feature Maps for Modeling and Control of Robotic Manipulators. Intelligent and Robotic Systems 30(4) (2003)
Bishop, C., Legleye, C.: Estimating conditional probability densities for periodic variables. In: Advances in Neural Information Processing Systems, vol. 7, pp. 641–648 (1995)
Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, New York (2002)
Klanke, S.: Learning Manifolds with the Parametrized Self-Organizing Map and Unsupervised Kernel Regression. PhD thesis, Bielefeld University (2007)
Mardia, K.: Statistics of Directional Data. Academic Press, London (1972)
Meinicke, P., Klanke, S., Memisevic, R., Ritter, H.: Principal Surfaces from Unsupervised Kernel Regression. IEEE Trans. on PAMI 27(9) (2005)
Nadaraya, E.A.: On Estimating Regression. Theory of Probability and Its Application 9 (1964)
Schölkopf, B., Smola, A., Müller, K.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10(5), 1299–1319 (1998)
Steffen, J., Haschke, R., Ritter, H.: Towards Dextrous Manipulation Using Manifolds. In: Proc. IROS (2008)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Walter, J., Ritter, H.: Rapid Learning with Parametrized Self-organizing Maps. Neurocomputing 12, 131–153 (1996)
Watson, G.S.: Smooth Regression Analysis. Sankhya, Ser. A 26 (1964)
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Steffen, J., Klanke, S., Vijayakumar, S., Ritter, H. (2009). Towards Semi-supervised Manifold Learning: UKR with Structural Hints. In: Príncipe, J.C., Miikkulainen, R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02397-2_34
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DOI: https://doi.org/10.1007/978-3-642-02397-2_34
Publisher Name: Springer, Berlin, Heidelberg
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