Concave losses for robust dictionary learning

Rafael Will M de Araujo (1) , Roberto Hirata (1) , Alain Rakotomamonjy (2)

1 USP - Universidade de São Paulo = University of São Paulo
2 DocApp - LITIS - Equipe Apprentissage

Traditional dictionary learning methods are based on quadratic convex loss function and thus are sensitive to outliers. In this paper, we propose a generic framework for robust dictionary learning based on concave losses. We provide results on composition of concave functions, notably regarding super-gradient computations, that are key for developing generic dictionary learning algorithms applicable to smooth and non-smooth losses. In order to improve identification of outliers, we introduce an initialization heuristic based on undercomplete dictionary learning. Experimental results using synthetic and real data demonstrate that our method is able to better detect outliers, is capable of generating better dictionaries, outperforming state-of-the-art methods such as K-SVD and LC-KSVD.

Mots clés: outlier detection; Robust dictionary learning; concave loss function

Domaines: Informatique; Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]

Rafael Will M de Araujo, Roberto Hirata, Alain Rakotomamonjy. Concave losses for robust dictionary learning. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, Calgary, Canada. ⟨hal-01627533⟩