Abstract
The sparse representation based classification (SRC) was initially proposed for face recognition problems. However, SRC was found to excel in a variety of classification tasks. There have been many extensions to SRC, of which group SRC, kernel SRC being the prominent ones. Prior methods in kernel SRC used greedy methods like Orthogonal Matching Pursuit (OMP). It is well known that for solving a sparse recovery problem, both in theory and in practice, l 1 -minimization is a better approach compared to OMP. The standard l 1 -minimization is a solved problem. For the first time in this work, we propose a technique for Kernel l 1 -minimization. Through simulation results we show that our proposed method outperforms prior kernelised greedy sparse recovery techniques.
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Gogna, A., Majumdar, A. (2016). Kernel L1-Minimization: Application to Kernel Sparse Representation Based Classification. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_16
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DOI: https://doi.org/10.1007/978-3-319-46672-9_16
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