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 Subspace techniques and biomedical time series analysis
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/5627

title: Subspace techniques and biomedical time series analysis
authors: Tomé, A. M.
Teixeira, A. R.
Lang, E. W.
keywords: Kernel methods
Projective subspace techniques
Time series analysis
issue date: 31-Jan-2012
publisher: Bentham Science Publishers
abstract: The application of subspace techniques to univariate (single-sensor) biomedical time series is presented. Both linear and non-linear methods are described using algebraic models, and the dot product is the most important operation concerning data manipulations. The covariance/correlationmatrices, computed in the space of time-delayed coordinates or in a feature space created by a non-linear mapping, are employed to deduce orthogonal models. Linear methods encompass singular spectrum analysis (SSA), singular value decomposition (SVD) or principal component analysis (PCA). Local SSA is a variant of SSA which can approximate non-linear trajectories of the embedded signal by introducing a clustering step. Generically non-linear methods encompass kernel principal component analysis (KPCA) and greedy KPCA. The latter is a variant where the subspace model is based on a selected subset of data only.
URI: http://hdl.handle.net/10773/5627
ISBN: 978-1-60805-218-9
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