Ecole d'ingénieur et centre de recherche en Sciences du numérique

Preprocessing for classification of sparse data : Application to trajectory recognition

Mayoue, Aurélien; Barthélemy, Quentin; Onis, Sébastien; Larue, Anthony

SSP 2012, IEEE Statistical Signal Processing Workshop, August 5-8, 2012, Ann Harbour, United States

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.

Doi Hal Bibtex

Titre:Preprocessing for classification of sparse data : Application to trajectory recognition
Mots Clés:Sparse coding, classification, multilayer perceptron, trajectories data
Type:Conférence
Langue:English
Ville:Ann Harbour
Pays:ÉTATS-UNIS
Date:
Département:Sécurité numérique
Eurecom ref:3813
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Bibtex: @inproceedings{EURECOM+3813, doi = {http://dx.doi.org/10.1109/SSP.2012.6319709}, year = {2012}, title = {{P}reprocessing for classification of sparse data : {A}pplication to trajectory recognition}, author = {{M}ayoue, {A}ur{\'e}lien and {B}arth{\'e}lemy, {Q}uentin and {O}nis, {S}{\'e}bastien and {L}arue, {A}nthony}, booktitle = {{SSP} 2012, {IEEE} {S}tatistical {S}ignal {P}rocessing {W}orkshop, {A}ugust 5-8, 2012, {A}nn {H}arbour, {U}nited {S}tates}, address = {{A}nn {H}arbour, {\'{E}}{TATS}-{UNIS}}, month = {08}, url = {http://www.eurecom.fr/publication/3813} }
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