Kernel Recursive Least-Squares Tracker for Time-Varying Regression

TitleKernel Recursive Least-Squares Tracker for Time-Varying Regression
Publication TypeJournal Article
Year of Publication2012
AuthorsS. Van Vaerenbergh, M. Lázaro-Gredilla, and I. Santamaría
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume23
Issue8
Pagination1313--1326
Month PublishedAugust
ISSN2162-237X
Keywordskernel adaptive filtering, KRLS
AbstractIn this paper, we introduce a kernel recursive least-squares (KRLS) algorithm that is able to track nonlinear, time-varying relationships in data. To this purpose, we first derive the standard KRLS equations from a Bayesian perspective (including a sensible approach to pruning) and then take advantage of this framework to incorporate forgetting in a consistent way, thus enabling the algorithm to perform tracking in nonstationary scenarios. The resulting method is the first kernel adaptive filtering algorithm that includes a forgetting factor in a principled and numerically stable manner. In addition to its tracking ability, it has a number of appealing properties. It is online, requires a fixed amount of memory and computation per time step, incorporates regularization in a natural manner and provides confidence intervals along with each prediction. We include experimental results that support the theory as well as illustrate the efficiency of the proposed algorithm.
URLhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6227361
DOI10.1109/TNNLS.2012.2200500
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