ICANN 2014: Artificial Neural Networks and Machine Learning – ICANN 2014 pp 121-128 | Cite as
Instance Selection Using Two Phase Collaborative Neighbor Representation
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.
Keywords
Instance selection collaborative neighbor representation video summarization classificationPreview
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References
- 1.Garcia, S., Derrac, J., Cano, R., Herrera, F.: Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(3), 417–435 (2012)CrossRefGoogle Scholar
- 2.Gu, M., Eisenstat, S.: Efficient algorithms for computing a strong rankrevealing qr factorization. SIAM Journal on Scientific Computing 17, 848–869 (1996)CrossRefMATHMathSciNetGoogle Scholar
- 3.Frey, B., Dueck, D.: Clustering by passing messages between data points. Science Magazine 315, 972–976 (2007)MATHMathSciNetGoogle Scholar
- 4.Tropp, J.: Column subset selection, matrix factorization and eigenvalue optimization. In: Proc. of ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 978–986 (January 2009)Google Scholar
- 5.Boutsidis, C., Mahoney, M., Drineas, P.: An improved approximation algorithm for the column subset selection problem. In: Proc. of ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 968–977 (January 2009)Google Scholar
- 6.Elhamifar, E., Sapiro, G., Vidal, R.: See all by looking at a few: Sparse modeling for finding representative objects. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1600–1607 (June 2012)Google Scholar
- 7.Bien, J., Xu, Y., Mahoney, M.: CUR from a sparse optimization viewpoint. In: Advances in Neural Information Processing Systems, pp. 217–225 (December 2010)Google Scholar
- 8.Chan, T.: Rank revealing qr factorizations. Linear Algebra and its Applications 88–89, 67–82 (1987)Google Scholar
- 9.Esser, E., Moller, M., Osher, S., Sapiro, G., Xin, J.: A convex model for nonnegative matrix factorization and dimensionality reduction on physical space. IEEE Transactions on Image Processing 21(7), 3239–3252 (2012)CrossRefMathSciNetGoogle Scholar
- 10.Charikar, M., Guha, S., Tardos, A., Shmoys, D.: A constant-factor approximation algorithm for the k-median problem. Journal of Computer System Sciences 65(1), 129–149 (2002)CrossRefMATHMathSciNetGoogle Scholar
- 11.Givoni, I., Chung, C., Frey, B.: Hierarchical affinity propagation. In: Conference on Uncertainty in Artificial Intelligence (July 2011)Google Scholar
- 12.Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience (2004)Google Scholar
- 13.Dueck, D., Frey, B.: Non-metric affinity propagation for unsupervised image categorization. In: Proc. of International Conference in Computer Vision, pp. 1–8 (October 2007)Google Scholar
- 14.Waqas, J., Yi, Z., Zhang, L.: Collaborative neighbor representation based classification using l 2-minimization approach. Pattern Recognition Letters 34(2), 201–208 (2013)CrossRefGoogle Scholar