Next, we present a benchmark study where 14 algorithms are evaluated on each of the time series in the data set.
In particular, we consider nonlinear Gaussian state-space models where sequential approximate inference results in the factorization of a data matrix into a dictionary and time-varying coefficients with potentially nonlinear Markovian dependencies.
The proposed empirical estimates of the Bayes error rate are computed from the minimal spanning tree (MST) of the samples from each pair of classes.
The SparseStep algorithm is presented for the estimation of a sparse parameter vector in the linear regression problem.
Sparse Learning Methodology 62J05, 62J07
Traditional extensions of the binary support vector machine (SVM) to multiclass problems are either heuristics or require solving a large dual optimization problem.