In this paper, we propose a novel multi-task multi-variate (MTMV) sparse representation method for multi-sensor classification, which takes into account correlations between sensors simultaneously while considering joint sparsity within each sensor's observations. This approach can be seen as the generalized model of multi-task and multivariate Lasso, where all the multi-sensor data are jointly represented by a sparse linear combination of training data. We further modify our MTMV model by including a clutter noise term that is also assume to be sparse in feature domain. An efficient algorithm based on alternative direction method is proposed for both models. Extensive experiments are conducted on real data set and the results are compared with the conventional discriminative classifiers to verify the effectiveness of the proposed methods.
['Nam H. Nguyen', 'Nasser M. Nasrabadi', 'Trac D. Tran']
Robust multi-sensor classification via joint sparse representation