  We present a new method for the classification of transient noise signals (or
glitches) in advanced gravitational-wave interferometers. The method uses
learned dictionaries (a supervised machine learning algorithm) for signal
denoising, and untrained dictionaries for the final sparse reconstruction and
classification. We use a data set of 3000 simulated glitches of three different
waveform morphologies, comprising 1000 glitches per morphology. These data are
embedded in non-white Gaussian noise to simulate the background noise of
advanced LIGO in its broadband configuration. Our classification method yields
a 96% accuracy for a large range of initial parameters, showing that learned
dictionaries are an interesting approach for glitch classification. This work
constitutes a preliminary step before assessing the performance of
dictionary-learning methods with actual detector glitches.
