Preprocessing for classification of sparse data: application to trajectory recognition

Aurélien Mayoue (1) , Quentin Barthélemy (1) , Sébastien Onis (1) , Anthony Larue (1)

1 LOAD (CEA, LIST) - Laboratoire Outils d'Analyse des Données (CEA, LIST)

On one hand, sparse coding, which is widely used in signal processing, consists of representing signals as linear combinations of few elementary patterns selected from a dedicated dictionary. The output is a sparse vector containing few coding coefficients and is called sparse code. On the other hand, Multilayer Perceptron (MLP) is a neural network classification method that learns non linear borders between classes using labeled data examples. The MLP input data are vectors, usually normalized and preprocessed to minimize the inter-class correlation. This article acts as a link between sparse coding and MLP by converting sparse code into convenient vectors for MLP input. This original association assures in this way the classification of any sparse signals. Experimental results obtained by the whole process on trajectories data and comparisons to other methods show that this approach is efficient for signals classification.

Mots clés: trajectories data; machine learning; dictionary learning; Sparse coding; signal processing; statistical analysis; classification; neural network; artificial intelligence; multilayer perceptron (MLP)

Domaines: Traitement du signal et de l'image [eess.SP]; Apprentissage [cs.LG]; Réseau de neurones [cs.NE]; Mathématique discrète [cs.DM]

Aurélien Mayoue, Quentin Barthélemy, Sébastien Onis, Anthony Larue. Preprocessing for classification of sparse data: application to trajectory recognition. 2012 IEEE workshop on Statistical Signal Processing (SSP 2012), Aug 2012, Ann Arbor, Michigan, United States. pp.37-40, ⟨10.1109/SSP.2012.6319709⟩. ⟨hal-00730423⟩