Abstract
Finding relevant instances in databases has always been a challenging task. Recently a new method, called Sparse Modeling Representative Selection (SMRS) has been proposed in this area and is based on data self-representation. SMRS estimates a matrix of coefficients by minimizing a reconstruction error and a regularization term on these coefficients using the L 1,q matrix norm. In this paper, we propose another alternative of coding based on a two stage Collaborative Neighbor Representation in which a non-dense matrix of coefficients is estimated without invoking any explicit sparse coding. Experiments are conducted on summarizing a video movie and on summarizing training face datasets used for face recognition. These experiments showed that the proposed method can outperform the state-of-the art methods.
This work was supported by the projects EHU13/40 and S-PR13UN007.
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Dornaika, F., Aldine, I.K. (2014). Instance Selection Using Two Phase Collaborative Neighbor Representation. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_16
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DOI: https://doi.org/10.1007/978-3-319-11179-7_16
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