Schirru, Andrea and Susto, Gian Antonio and Pampuri, Simone and McLoone, Sean
Learning from Time Series: Supervised Aggregative Feature Extraction.
51st Annual Conference on Decision and Control (CDC).
IEEE, pp. 5254-5259.
Many modeling problems require to estimate a
scalar output from one or more time series. Such problems
are usually tackled by extracting a fixed number of features
from the time series (like their statistical moments), with a
consequent loss in information that leads to suboptimal predictive
models. Moreover, feature extraction techniques usually
make assumptions that are not met by real world settings (e.g.
uniformly sampled time series of constant length), and fail
to deliver a thorough methodology to deal with noisy data.
In this paper a methodology based on functional learning
is proposed to overcome the aforementioned problems; the
proposed Supervised Aggregative Feature Extraction (SAFE)
approach allows to derive continuous, smooth estimates of
time series data (yielding aggregate local information), while
simultaneously estimating a continuous shape function yielding
optimal predictions. The SAFE paradigm enjoys several
properties like closed form solution, incorporation of first and
second order derivative information into the regressor matrix,
interpretability of the generated functional predictor and the
possibility to exploit Reproducing Kernel Hilbert Spaces setting
to yield nonlinear predictive models. Simulation studies are
provided to highlight the strengths of the new methodology
w.r.t. standard unsupervised feature selection approaches.
||The definitive version of this article is available at 10.1109/CDC.2012.6427099
||Kernel Hilbert Spaces; SAFE; feature extraction techniques; fixed number; functional learning; nonlinear predictive models; scalar output; statistical moments; suboptimal predictive models; supervised aggregative feature extraction; time series data; time series learning;
||Faculty of Science and Engineering > Electronic Engineering
||27 Feb 2013 16:14
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||51st IEEE Conference on Decision and Control, Proceedings
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