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Machine Learning

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Regularized Sparse Kernel Slow Feature Analysis
Citation key Boehmer2011
Author Böhmer, W. and Grünewälder, S. and Nickisch, H. and Obermayer, K.
Title of Book Lecture Notes in Computer Science
Pages 235–248
Year 2011
Volume 6911
Month September
Editor Gunopulos, D.; Hofmann, Th.; Malerba, D.; Vazirgiannis, M.
Publisher Springer-Verlag Berlin Heidelberg
Series LNAI 6911
Chapter Machine Learning and Knowledge Discovery in Databases
Abstract This paper develops a kernelized slow feature analysis (SFA) algorithm. SFA is an unsupervised learning method to extract features which encode latent variables from time series. Generative relationships are usually complex, and current algorithms are either not powerful enough or tend to over-fit. We make use of the kernel trick in combi- nation with sparsification to provide a powerful function class for large data sets. Sparsity is achieved by a novel matching pursuit approach that can be applied to other tasks as well. For small but complex data sets, however, the kernel SFA approach leads to over-fitting and numeri- cal instabilities. To enforce a stable solution, we introduce regularization to the SFA objective. Versatility and performance of our method are demonstrated on audio and video data sets.
Bibtex Type of Publication Selected:reinforcement
Link to original publication Download Bibtex entry

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