jmlr jmlr2011 jmlr2011-55 jmlr2011-55-reference knowledge-graph by maker-knowledge-mining
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Author: Brian McFee, Gert Lanckriet
Abstract: In many applications involving multi-media data, the definition of similarity between items is integral to several key tasks, including nearest-neighbor retrieval, classification, and recommendation. Data in such regimes typically exhibits multiple modalities, such as acoustic and visual content of video. Integrating such heterogeneous data to form a holistic similarity space is therefore a key challenge to be overcome in many real-world applications. We present a novel multiple kernel learning technique for integrating heterogeneous data into a single, unified similarity space. Our algorithm learns an optimal ensemble of kernel transformations which conform to measurements of human perceptual similarity, as expressed by relative comparisons. To cope with the ubiquitous problems of subjectivity and inconsistency in multimedia similarity, we develop graph-based techniques to filter similarity measurements, resulting in a simplified and robust training procedure. Keywords: multiple kernel learning, metric learning, similarity
Sameer Agarwal, Joshua Wills, Lawrence Cayton, Gert Lanckriet, David Kriegman, and Serge Belongie. Generalized non-metric multi-dimensional scaling. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2007. Alfred V. Aho, Michael R. Garey, and Jeffrey D. Ullman. The transitive reduction of a directed graph. SIAM Journal on Computing, 1(2):131–137, 1972. doi: 10.1137/0201008. URL http: //link.aip.org/link/?SMJ/1/131/1. Anelia Angelova. Data pruning. Master’s thesis, California Institute of Technology, 2004. Anelia Angelova, Yaser Abu-Mostafa, and Pietro Perona. Pruning training sets for learning of object categories. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 1: 494–501, 2005. ISSN 1063-6919. doi: http://doi.ieeecomputersociety.org/10.1109/CVPR.2005. 283. Dana Angluin and Philip Laird. Learning from noisy examples. Mach. Learn., 2(4):343–370, 1988. ISSN 0885-6125. doi: http://dx.doi.org/10.1023/A:1022873112823. Francis R. Bach. Consistency of the group lasso and multiple kernel learning. J. Mach. Learn. Res., 9:1179–1225, 2008. ISSN 1532-4435. Yoshua Bengio, Jean-François Paiement, Pascal Vincent, Olivier Delalleau, Nicolas Le Roux, and Marie Ouimet. Out-of-sample extensions for lle, isomap, mds, eigenmaps, and spectral clustering. In Sebastian Thrun, Lawrence Saul, and Bernhard Schölkopf, editors, Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2004. 520 L EARNING M ULTI - MODAL S IMILARITY Bonnie Berger and Peter W. Shor. Approximation alogorithms for the maximum acyclic subgraph problem. In SODA ’90: Proceedings of the First Annual ACM-SIAM Symposium on Discrete algorithms, pages 236–243, Philadelphia, PA, USA, 1990. Society for Industrial and Applied Mathematics. ISBN 0-89871-251-3. Mikhail Bilenko, Sugato Basu, and Raymond J. Mooney. Integrating constraints and metric learning in semi-supervised clustering. In Proceedings of the Twenty-first International Conference on Machine Learning, pages 81–88, 2004. Ingwer Borg and Patrick J.F. Groenen. Modern Multidimensional Scaling: Theory and Applications. Springer-Verlag, second edition, 2005. Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004. Oscar Celma. Music Recommendation and Discovery in the Long Tail. PhD thesis, Universitat Pompeu Fabra, Barcelona, Spain, 2008. Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh. Learning non-linear combinations of kernels. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 396–404. 2009. Trevor F. Cox and Michael A.A. Cox. Multidimensional Scaling. Chapman and Hall, 1994. Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, and Inderjit S. Dhillon. Information-theoretic metric learning. In ICML ’07: Proceedings of the 24th International Conference on Machine Learning, pages 209–216, New York, NY, USA, 2007. ACM. ISBN 978-1-59593-793-3. doi: http://doi.acm.org/10.1145/1273496.1273523. Steven B. Davis and Paul Mermelstein. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. pages 65–74, 1990. Daniel P.W. Ellis, Brian Whitman, Adam Berenzweig, and Steve Lawrence. The quest for ground truth in musical artist similarity. In Proeedings of the International Symposium on Music Information Retrieval (ISMIR2002), pages 170–177, October 2002. Michael R. Garey and David S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York, NY, USA, 1979. ISBN 0716710447. Jan-Mark Geusebroek, Gertjan J. Burghouts, and Arnold W. M. Smeulders. The Amsterdam library of object images. Int. J. Comput. Vis., 61(1):103–112, 2005. Amir Globerson and Sam Roweis. Metric learning by collapsing classes. In Yair Weiss, Bernhard Schölkopf, and John Platt, editors, Advances in Neural Information Processing Systems 18, pages 451–458, Cambridge, MA, 2006. MIT Press. Amir Globerson and Sam Roweis. Visualizing pairwise similarity via semidefinite embedding. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2007. 521 M C F EE AND L ANCKRIET Jacob Goldberger, Sam Roweis, Geoffrey Hinton, and Ruslan Salakhutdinov. Neighborhood components analysis. In Lawrence K. Saul, Yair Weiss, and Léon Bottou, editors, Advances in Neural Information Processing Systems 17, pages 513–520, Cambridge, MA, 2005. MIT Press. Saketha Nath Jagarlapudi, Dinesh G, Raman S, Chiranjib Bhattacharyya, Aharon Ben-Tal, and Ramakrishnan K.R. On the algorithmics and applications of a mixed-norm based kernel learning formulation. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 844–852. 2009. Tony Jebara, Risi Kondor, and Andrew Howard. Probability product kernels. Journal of Machine Learning Research, 5:819–844, 2004. ISSN 1533-7928. Maurice Kendall and Jean Dickinson Gibbons. Rank correlation methods. Oxford University Press, 1990. Marius Kloft, Ulf Brefeld, Soeren Sonnenburg, Pavel Laskov, Klaus-Robert Müller, and Alexander Zien. Efficient and accurate lp-norm multiple kernel learning. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 997–1005. 2009. Joseph B. Kruskal. Nonmetric multidimensional scaling: a numerical method. Psychometrika, 29 (2), June 1964. Gert R. G. Lanckriet, Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, and Michael I. Jordan. Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res., 5:27–72, 2004. ISSN 1533-7928. Yen-Yu Lin, Tyng-Luh Liu, and Chiou-Shann Fuh. Dimensionality reduction for data in multiple feature representations. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, pages 961–968. 2009. Brian McFee and Gert R. G. Lanckriet. Heterogeneous embedding for subjective artist similarity. In Tenth International Symposium for Music Information Retrieval (ISMIR2009), October 2009a. Brian McFee and Gert R. G. Lanckriet. Partial order embedding with multiple kernels. In Proceedings of the 26th International Conference on Machine Learning (ICML’09), pages 721–728, June 2009b. Jaroslav Opatrny. Total ordering problem. SIAM J. Computing, (8):111–114, 1979. Volker Roth, Julian Laub, Joachim M. Buhmann, and Klaus-Robert Müller. Going metric: denoising pairwise data. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 809–816, Cambridge, MA, 2003. MIT Press. Bernhard Schölkopf, Ralf Herbrich, Alex J. Smola, and Robert Williamson. A generalized representer theorem. In Proceedings of the 14th Annual Conference on Computational Learning Theory, pages 416–426, 2001. Matthew Schultz and Thorsten Joachims. Learning a distance metric from relative comparisons. In Sebastian Thrun, Lawrence Saul, and Bernhard Schölkopf, editors, Advances in Neural Information Processing Systems 16, Cambridge, MA, 2004. MIT Press. 522 L EARNING M ULTI - MODAL S IMILARITY Noam Shental, Tomer Hertz, Daphna Weinshall, and Misha Pavel. Adjustment learning and relevant components analysis. In European Conference on Computer Vision, 2002. Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, and Bernhard Schölkopf. Large scale multiple kernel learning. J. Mach. Learn. Res., 7:1531–1565, 2006. ISSN 1532-4435. Warren S. Torgerson. Multidimensional scaling: 1. theory and method. Psychometrika, 17:401–419, 1952. Douglas Turnbull, Luke Barrington, David Torres, and Gert Lanckriet. Semantic annotation and retrieval of music and sound effects. IEEE Transactions on Audio, Speech and Language Processing, 16(2):467–476, February 2008. Laurens van der Maaten and Geoffrey Hinton. Visualizing high-dimensional data using t-SNE. Journal of Machine Learning Research, 9:2579–2605, 2008. Alexander Vezhnevets and Olga Barinova. Avoiding boosting overfitting by removing confusing samples. In ECML 2007, pages 430–441, 2007. Kiri Wagstaff, Claire Cardie, Seth Rogers, and Stefan Schroedl. Constrained k-means clustering with background knowledge. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 577–584, 2001. Kilian Q. Weinberger, John Blitzer, and Lawrence K. Saul. Distance metric learning for large margin nearest neighbor classification. In Yair Weiss, Bernhard Schölkopf, and John Platt, editors, Advances in Neural Information Processing Systems 18, pages 451–458, Cambridge, MA, 2006. MIT Press. Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, and Stuart Russell. Distance metric learning, with application to clustering with side-information. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 505–512, Cambridge, MA, 2003. MIT Press. Alexander Zien and Cheng Soon Ong. Multiclass multiple kernel learning. In ICML ’07: Proceedings of the 24th International Conference on Machine Learning, pages 1191–1198, New York, NY, USA, 2007. ACM. ISBN 978-1-59593-793-3. doi: http://doi.acm.org/10.1145/1273496. 1273646. 523