TY - JOUR
T1 - Joint-structured-sparsity-based classification for multiple-measurement transient acoustic signals
AU - Zhang, Haichao
AU - Zhang, Yanning
AU - Nasrabadi, Nasser M.
AU - Huang, Thomas S.
N1 - Funding Information:
Manuscript received May 5, 2011; revised November 16, 2011; accepted April 15, 2012. Date of publication May 15, 2012; date of current version November 14, 2012. This work was supported in part by the National Natural Science Foundation of China under Grant 60872145 and Grant 60903126, by the National High Technology Research and Development Program (863) of China under Grant 2009AA01Z315, by the China Postdoctoral (Special) Science Foundation under Grant 20090451397 and Grant 201003685, by the Cultivation Fund of the Key Scientific and Technical Innovation Project from the Ministry of Education of China under Grant 708085, and by the U.S. Army Research Laboratory and U.S. Army Research Office under Grant W911NF-09-1-0383. This paper was recommended by Associate Editor N. Chawla.
PY - 2012
Y1 - 2012
N2 - This paper investigates the joint-structured-sparsity-based methods for transient acoustic signal classification with multiple measurements. By joint structured sparsity, we not only use the sparsity prior for each measurement but we also exploit the structural information across the sparse representation vectors of multiple measurements. Several different sparse prior models are investigated in this paper to exploit the correlations among the multiple measurements with the notion of the joint structured sparsity for improving the classification accuracy. Specifically, we propose models with the joint structured sparsity under different assumptions: same sparse code model, common sparse pattern model, and a newly proposed joint dynamic sparse model. For the joint dynamic sparse model, we also develop an efficient greedy algorithm to solve it. Extensive experiments are carried out on real acoustic data sets, and the results are compared with the conventional discriminative classifiers in order to verify the effectiveness of the proposed method.
AB - This paper investigates the joint-structured-sparsity-based methods for transient acoustic signal classification with multiple measurements. By joint structured sparsity, we not only use the sparsity prior for each measurement but we also exploit the structural information across the sparse representation vectors of multiple measurements. Several different sparse prior models are investigated in this paper to exploit the correlations among the multiple measurements with the notion of the joint structured sparsity for improving the classification accuracy. Specifically, we propose models with the joint structured sparsity under different assumptions: same sparse code model, common sparse pattern model, and a newly proposed joint dynamic sparse model. For the joint dynamic sparse model, we also develop an efficient greedy algorithm to solve it. Extensive experiments are carried out on real acoustic data sets, and the results are compared with the conventional discriminative classifiers in order to verify the effectiveness of the proposed method.
KW - Joint sparse representation
KW - joint structured sparsity
KW - multiple-measurement transient acoustic signal classification
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U2 - 10.1109/TSMCB.2012.2196038
DO - 10.1109/TSMCB.2012.2196038
M3 - Article
C2 - 22614692
AN - SCOPUS:84869497089
SN - 1083-4419
VL - 42
SP - 1586
EP - 1598
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 6
M1 - 6200352
ER -