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Kernel recursive least-squares tracker for time-varying regression.

Authors
Type
Published Article
Journal
IEEE transactions on neural networks and learning systems
Publication Date
Volume
23
Issue
8
Pages
1313–1326
Identifiers
DOI: 10.1109/TNNLS.2012.2200500
PMID: 24807527
Source
Medline
License
Unknown

Abstract

In 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.

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