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Accession Number ADA565010
Title Robust Multi-Sensor Classification via Joint Sparse Representation.
Publication Date Jul 2011
Media Count 9p
Personal Author N. H. Nguyen N. M. Nasrabadi T. D. Tran
Abstract In this paper, we propose a novel multi-task multivariate (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.
Keywords Acoustics
Algorithms
Classification
Efficiency
Maximum a posterior
Multi task multi variate
Multisensors
Semantics
Signal processing
Support vector machine

 
Source Agency Non Paid ADAS
NTIS Subject Category 63F - Optical Detection
46A - Acoustics
Corporate Author Johns Hopkins Univ., Baltimore, MD. Dept. of Electrical Engineering and Computer Science.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1304
Contract Number N/A

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