Nearest hyperdisk methods for high-dimensional classification

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 120-127

  Cevikalp, Hakan ^a   Triggs, Bill ^b   Polikar, Robi ^c  

^a ESKISEHIR OSMANGAZI UNIVERSITY   (Turkey)

^b UNIV GRENOBLE ALPES   (France)

^c ROWAN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL GEOMETRY; MACHINE LEARNING; NEAREST NEIGHBOR SEARCH; SAMPLING; CLASSIFIERS; COMPUTATIONAL COMPLEXITY; ROBOT LEARNING;

APPROXIMATE MODEL; CONVEX MODELING; DECISION BOUNDARY; HIGH-DIMENSIONAL; LOCAL CLASSIFIER; NEAREST NEIGHBORS; NON-EUCLIDEAN DISTANCE; NONLINEAR CLASSIFIERS;

CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS;

AFFINE HULLS; APPROXIMATE MODELS; CONVEX HULLS; CONVEX MODELS; DECISION BOUNDARIES; EUCLIDEAN DISTANCE METRICS; HIGH-DIMENSIONAL; HYPER-SPHERES; KERNEL METHODS; LOCAL CLASSIFIERS; NON-LINEAR CLASSIFIERS; OVER FITTINGS; SPARSE SAMPLES; TRAINING SAMPLES;

EID: 56449118263     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390172     Document Type: Conference Paper

References (17)

1. Bennett, K. P., & Bredensteiner, E. J. (2000). Duality and geometry in svm classifiers. ICML.
2. Cevikalp, H., Larlus, D., Douze, M., & Jurie, F. (2007). Local subspace classifiers: Linear and nonlinear approaches. IEEE Workshop on Machine Learning for Signal Processing. Thessaloniki, Greece.
3. Cevikalp, H., Neanrtu, M., Wilkes, M., & Barkana, A. (2005). Discriminative common vectors for face recognition. IEEE Transactions on PAMI, 27, 4-13.
4. Cevikalp, H., Triggs, B., Jurie, F., & Polikar, R. (2008). Margin-based discriminant dimensionality reduction for visual recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
5. Gulmezoglu, M. B., Dzhafarov, V., & Barkana, A. (2001). The common vector approach and its relation to principal component analysis. IEEE Trans. Speech Audio Proc., 9, 655-662.
6. Hinton, G. E., Dayan, P., & Revow, M. (1997). Modeling the manifolds of images of handwritten digits. IEEE Transactions on Neural Networks, 18, 65-74.
7. Laaksonen, J. (1997). Subspace classifiers in recognition of handwritten digits. Doctoral dissertation. Helsinki University of Technology.
8. Lazebnik, S., Schmid, C. & Ponce, J. (2005). A maximum entropy framework for part-based texture and object recognition. International Conference on Computer Vision.
9. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 91-110.
10. Nalbantov, G. I., Groenen, P. J. F., & Bioch, J. C. (2007). Nearest convex hull classification (Technical Report). Econometric Institute and Erasmus Research Institute of Management.
11. Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2323-2326.
12. Schölkopf, B., Smola, A. J., & Muller, K. R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10, 1299-1319.
13. Shawe-Taylor, L. & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge University Press.
14. Tax, D. M. J., & Duin, R. P. W. (2004). Support vector data description. Machine Learning, 54, 45-66.
15. Verbeek, J. (2006). Learning non-linear image manifolds by global alignment of local linear models. IEEE Transactions on PAMI, 28, 1236-1250.
16. Vincent, P., & Bengio, Y. (2001). K-local hyperplane and convex distance nearest neighbor algorithms. NIPS.
17. Wang, J., Neskovic, P., & Cooper, L. N. (2005). Pattern classification via single spheres. Discovery Science (pp. 241-252).