  In this work we propose Support Vector Machine classification algorithms to
classify onedimensional crystal lattice waves from locally sampled data.
Different learning datasets of particle displacements, momenta and energy
density values are considered. Efficiency of the classification algorithms is
further improved by two dimensionality reduction techniques: Principal
Component Analysis and Locally Linear Embedding. Robustness of classifiers is
investigated and demonstrated. Developed algorithms are successfully applied to
detect localized intrinsic modes in three numerical simulations considering a
case of two localized stationary breather solutions, a single stationary
breather solution in noisy background and two mobile breather collision.
