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Regularized Sparse Kernel Slow Feature Analysis
Zitatschlüssel Boehmer2011
Autor Böhmer, W. and Grünewälder, S. and Nickisch, H. and Obermayer, K.
Buchtitel Lecture Notes in Computer Science
Seiten 235–248
Jahr 2011
Jahrgang 6911
Monat September
Herausgeber Gunopulos, D.; Hofmann, Th.; Malerba, D.; Vazirgiannis, M.
Verlag Springer-Verlag Berlin Heidelberg
Serie LNAI 6911
Kapitel Machine Learning and Knowledge Discovery in Databases
Zusammenfassung 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.
Typ der Publikation Selected:reinforcement
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