Nonparametric Basis Pursuit via Sparse Kernel-Based Learning

被引:50
作者
Bazerque, Juan Andres [1 ]
Giannakis, Georgios B. [2 ,3 ]
机构
[1] Univ Minnesota, Minneapolis, MN 55455 USA
[2] Univ Minnesota, Dept Elect & Comp Engn, Minneapolis, MN 55455 USA
[3] Univ Minnesota, Digital Technol Ctr, Minneapolis, MN 55455 USA
关键词
SPLINES;
D O I
10.1109/MSP.2013.2253354
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Signal processing tasks as fundamental as sampling, reconstruction, minimum mean-square error interpolation, and prediction can be viewed under the prism of reproducing kernel Hilbert spaces (RKHSs). Endowing this vantage point with contemporary advances in sparsity-aware modeling and processing promotes the nonparametric basis pursuit advocated in this article as the overarching framework for the confluence of kernel-based learning (KBL) approaches leveraging sparse linear regression, nuclear-norm regularization, and dictionary learning. The novel sparse KBL toolbox goes beyond translating sparse parametric approaches to their nonparametric counterparts to incorporate new possibilities such as multikernel selection and matrix smoothing. The impact of sparse KBL to signal processing applications is illustrated through test cases from cognitive radio sensing, microarray data imputation, and network traffic prediction. © 1991-2012 IEEE.
引用
收藏
页码:112 / 125
页数:14
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