Abstract
Graph-based methods are very popular in semi-supervised learning due to their well founded theoretical background, intuitive interpretation of local neighborhood structure, and strong performance on a wide range of challenging learning problems. However, the success of these methods is highly dependent on the pre-existing neighborhood structure in the data used to construct the graph. In this paper, we use metric learning to improve this critical step by increasing the precision of the nearest neighbors and building our graph in this new metric space. We show that learning of neighborhood relations before constructing the graph consistently improves performance of two label propagation schemes on three different datasets – achieving the best performance reported on Caltech 101 to date. Furthermore, we question the predominant random draw of labels and advocate the importance of the choice of labeled examples. Orthogonal to active learning schemes, we investigate how domain knowledge can substantially increase performance in these semi-supervised learning settings.
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References
Boiman, O., Shechtman, E., Irani, M.: In defense of Nearest-Neighbor based image classification. In: CVPR (2008)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR (2005)
Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: ICML (2007)
Ebert, S., Larlus, D., Schiele, B.: Extracting structures in image collections for object recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 720–733. Springer, Heidelberg (2010)
Everingham, M., Van Gool, L., Williams, C.K.: The PASCAL VOC (2008)
Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. PAMI 28(4), 594–611 (2006)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI 32, 1627–1645 (2010)
Gehler, P., Nowozin, S.: On feature combination for multiclass object classification. In: ICCV (2009)
Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood components analysis. In: NIPS (2005)
Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)
Grauman, K., Darrell, T.: The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. In: ICCV (2005)
Jain, P., Kapoor, A.: Active learning for large multi-class problems. In: CVPR (2009)
Jebara, T., Wang, J., Chang, S.F.: Graph construction and b -matching for semi-supervised learning. In: ICML (2009)
Kulis, B., Jain, P., Grauman, K.: Fast Similarity Search for Learned Metrics. PAMI 31(12), 2143–2157 (2009)
Leibe, B., Schiele, B.: Analyzing Appearance and Contour Based Methods for Object Categorization. In: CVPR (2003)
Lu, Z., Jain, P., Dhillon, I.S.: Geometry-aware metric learning. In: ICML (2009)
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010)
Teramoto, R.: Prediction of Alzheimer’s diagnosis using semi-supervised distance metric learning with label propagation. Comp. Biol. and Chem. 32(6), 438–441 (2008)
Wang, F., Zhang, C.: Label propagation through linear neighborhoods. KDE 20(1), 55–67 (2008)
Weinberger, K.Q., Saul, L.K.: Distance Metric Learning for Large Margin Nearest Neighbor Classification. JMLR 10, 207–244 (2009)
Welinder, P., Branson, S., Belongie, S., Perona, P.: The multidimensional wisdom of crowds. In: NIPS (2010)
Zhou, D., Schölkopf, B., Bousquet, O., Lal, T.N., Weston, J.: Learning with Local and Global Consistency. In: NIPS (2004)
Zhu, X., Lafferty, J.: Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning. In: ICML (2005)
Zhu, X.: Semi-supervised learning literature survey. Tech. rep., University of Wisconsin-Madison (2006)
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Ebert, S., Fritz, M., Schiele, B. (2011). Pick Your Neighborhood – Improving Labels and Neighborhood Structure for Label Propagation. In: Mester, R., Felsberg, M. (eds) Pattern Recognition. DAGM 2011. Lecture Notes in Computer Science, vol 6835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23123-0_16
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DOI: https://doi.org/10.1007/978-3-642-23123-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23122-3
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