nips nips2008 nips2008-63 nips2008-63-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Yen-yu Lin, Tyng-luh Liu, Chiou-shann Fuh
Abstract: In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. These representations are typically high dimensional and assume diverse forms. Thus finding a way to transform them into a unified space of lower dimension generally facilitates the underlying tasks, such as object recognition or clustering. We describe an approach that incorporates multiple kernel learning with dimensionality reduction (MKL-DR). While the proposed framework is flexible in simultaneously tackling data in various feature representations, the formulation itself is general in that it is established upon graph embedding. It follows that any dimensionality reduction techniques explainable by graph embedding can be generalized by our method to consider data in multiple feature representations.
[1] A. Berg, T. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. In CVPR, 2005.
[2] A. Bosch, A. Zisserman, and X. Mu˜ oz. Image classification using random forests and ferns. In ICCV, n 2007.
[3] H.-T. Chen, H.-W. Chang, and T.-L. Liu. Local discriminant embedding and its variants. In CVPR, 2005.
[4] L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In CVPR Workshop on Generative-Model Based Vision, 2004.
[5] A. Frome, Y. Singer, and J. Malik. Image retrieval and classification using local distance functions. In NIPS, 2006.
[6] K. Grauman and T. Darrell. The pyramid match kernel: Efficient learning with sets of features. JMLR, 2007.
[7] X. He and P. Niyogi. Locality preserving projections. In NIPS, 2003.
[8] S.-J. Kim, A. Magnani, and S. Boyd. Optimal kernel selection in kernel fisher discriminant analysis. In ICML, 2006.
[9] G. Lanckriet, N. Cristianini, P. Bartlett, L. Ghaoui, and M. Jordan. Learning the kernel matrix with semidefinite programming. JMLR, 2004.
[10] Y.-Y. Lin, T.-L. Liu, and C.-S. Fuh. Local ensemble kernel learning for object category recognition. In CVPR, 2007.
[11] D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2004.
[12] S. Mika, G. R¨ tsch, J. Weston, B. Sch¨ lkopf, and K.-R. M¨ ller. Fisher discriminant analysis with kernels. a o u In Neural Networks for Signal Processing, 1999.
[13] J. Mutch and D. Lowe. Multiclass object recognition with sparse, localized features. In CVPR, 2006.
[14] A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. More efficiency in multiple kernel learning. In ICML, 2007.
[15] T. Serre, L. Wolf, and T. Poggio. Object recognition with features inspired by visual cortex. In CVPR, 2005.
[16] S. Sonnenburg, G. R¨ tsch, C. Sch¨ fer, and B. Sch¨ lkopf. Large scale multiple kernel learning. JMLR, a a o 2006.
[17] L. Vandenberghe and S. Boyd. Semidefinite programming. SIAM Review, 1996.
[18] M. Varma and D. Ray. Learning the discriminative power-invariance trade-off. In ICCV, 2007.
[19] S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, and S. Lin. Graph embedding and extensions: A general framework for dimensionality reduction. PAMI, 2007.
[20] H. Zhang, A. Berg, M. Maire, and J. Malik. Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In CVPR, 2006.
[21] J. Zhu, S. Rosset, H. Zou, and T. Hastie. Multi-class adaboost. Technical report, Dept. of Statistics, University of Michigan, 2005.