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
Keywords: Kernel methods, projective subspace techniques, time series analysis.
