Cite

Agarwal, S., Awan, A. and Roth, D. (2004). Learning to detect objects in images via a sparse, part-based representation, IEEE Transactions on Pattern Analysis and Machine Intelligence26(11): 1475–1490.10.1109/TPAMI.2004.10815521495Search in Google Scholar

Bååth, R. (2014). Bayesian first aid: A package that implements Bayesian alternatives to the classical *.test functions in R, International R User Conference UseR! 2014, Los Angeles, CA, USA, pp. 86.Search in Google Scholar

Baudat, G. and Anouar, F. (2000). Generalized discriminant analysis, Neural Computation12(1): 2385–2404.10.1162/08997660030001498011032039Search in Google Scholar

Boutsidis, C., Zouzias, A., Mahoney, M.W. and Drineas, P. (2011). Stochastic dimensionality reduction for k-means clustering, CoRRabs/1110.2897, http://arxiv.org/abs/1110.2897.Search in Google Scholar

Bronstein, A.M., Bronstein, M.M. and Ovsjanikov, M. (2010). Feature-based methods in 3D shape analysis, in N. Pears et al. (Eds.), 3D Imaging Analysis and Applications, Springer-Verlag, London, pp. 185–216.Search in Google Scholar

Chai, D., He, X., Zhou, K., Han, J. and Bao, H. (2007). Locality sensitive discriminant analysis, International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 708–713.Search in Google Scholar

Cunningham, J.P. and Ghahramani, Z. (2015). Linear dimensionality reduction: Survey, insights, and generalizations, Journal of Machine Learning Research16(1): 2859–2900.Search in Google Scholar

Demirci, M.F., Osmanlioglu, Y., Shokoufandeh, A. and Dickinson, S. (2011). Efficient many-to-many feature matching under the l1 norm, Computer Vision and Image Understanding115(7): 967–983.10.1016/j.cviu.2010.12.012Search in Google Scholar

Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society39(1): 1–38.10.1111/j.2517-6161.1977.tb01600.xSearch in Google Scholar

Fei-Fei, L., Fergus, R. and Perona, P. (2007). Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories, Journal Computer Vision and Image Understanding106(1): 59–70.10.1016/j.cviu.2005.09.012Search in Google Scholar

Fei-Fei, L. and Perona, P. (2005). A Bayesian hierarchical model for learning natural scene categories, IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, pp. 524–531.Search in Google Scholar

Felzenszwalb, P.F., Girshick, R.B., McAllester, D. and Ramanan, D. (2010). Object detection with discriminatively trained part-based models, IEEE Transactions on Pattern Analysis and Machine Intelligence32(9): 1627–1645.10.1109/TPAMI.2009.16720634557Search in Google Scholar

Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition, Academic Press, San Diego, CA.10.1016/B978-0-08-047865-4.50007-7Search in Google Scholar

Gkalelis, N., Mezaris, V. and Kompatsiaris, I. (2011). Mixture subclass discriminant analysis, IEEE Signal Processing Letters18(5): 319–322.10.1109/LSP.2011.2127474Search in Google Scholar

Górecki, T. and Łuczak, M. (2013). Linear discriminant analysis with a generalization of the Moore–Penrose pseudoinverse, International Journal of Applied Mathematics and Computer Science23(2): 463–471, DOI: 10.2478/amcs-2013-0035.10.2478/amcs-2013-0035Search in Google Scholar

Harandi, M.T., Sanderson, C., Shirazi, S. and Lovell, B.C. (2011). Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA, pp. 2705–2712.Search in Google Scholar

Hastie, T., Buja, A. and Tibshirani, R. (1995). Penalized discriminant analysis, Annals of Statistics23(1): 73–102.10.1214/aos/1176324456Search in Google Scholar

eISSN:
2083-8492
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Mathematics, Applied Mathematics