Collaborative Multi-sensor Classification via Sparsity-based Representation
This work addresses classification problems for applications using multiple heterogeneous sensors, but it appears incremental as it builds on existing sparse representation methods.
The paper tackles multi-sensor classification by proposing a collaborative sparse representation framework that accounts for correlations and complementary information between sensors while handling sparse noise and low-rank interference, with experiments on real datasets showing effectiveness compared to conventional classifiers.
In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations. We also robustify our models to deal with the presence of sparse noise and low-rank interference signals. Specifically, we demonstrate that incorporating the noise or interference signal as a low-rank component in our models is essential in a multi-sensor classification problem when multiple co-located sources/sensors simultaneously record the same physical event. We further extend our frameworks to kernelized models which rely on sparsely representing a test sample in terms of all the training samples in a feature space induced by a kernel function. A fast and efficient algorithm based on alternative direction method is proposed where its convergence to an optimal solution is guaranteed. Extensive experiments are conducted on several real multi-sensor data sets and results are compared with the conventional classifiers to verify the effectiveness of the proposed methods.