论文信息 - Comparison of Two Feature Extraction Methods Based on Maximization of Mutual Information

Comparison of Two Feature Extraction Methods Based on Maximization of Mutual Information

We perform a detailed comparison of two feature extraction methods that are based on mutual information maximization between the data points projected in the developed subspace and their class labels. For the simulations, we use synthetic as well as publicly available real-world data sets.

M.M. Van Hulle | N. Chumerin | N. Chumerin | M. V. Van Hulle

[1] Aapo Hyvärinen,et al. New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit , 1997, NIPS.

[2] J. Edward Jackson,et al. A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

[3] Forrest W. Young. Multidimensional Scaling: History, Theory, and Applications , 1987 .

[4] Joydeep Ghosh,et al. Linear feature extractors based on mutual information , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[5] J. Edward Jackson,et al. A User's Guide to Principal Components. , 1991 .

[6] A. Kraskov,et al. Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7] J. E. Jackson. A User's Guide to Principal Components , 1991 .

[8] Deniz Erdogmus,et al. Feature extraction using information-theoretic learning , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9] Antonio Artés-Rodríguez,et al. Maximization of Mutual Information for Supervised Linear Feature Extraction , 2007, IEEE Transactions on Neural Networks.

[10] E. Parzen. On Estimation of a Probability Density Function and Mode , 1962 .

[11] Deniz Erdogmus,et al. Sequential Feature Extraction Using Information-Theoretic Learning , 2001 .

[12] William M. Campbell,et al. Mutual Information in Learning Feature Transformations , 2000, ICML.