Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Feature Selection and Dualities in Maximum Entropy Discrimination
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
The Journal of Machine Learning Research
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Automatic clinical image segmentation using pathological modelling, PCA and SVM
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Functional Feature Selection by Weighted Projections in Pathological Voice Detection
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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Approaches based on obtaining relevant information from overwhelmingly large sets of measures have been recently adopted as an alternative to specialized features. In this work, we address the problem of finding a relevant subset of features and a suitable rotation (combined feature selection and feature extraction) as a weighted rotation. We focus our attention on two types of rotations: Weighted Principal Component Analysis and Weighted Regularized Discriminant Analysis. The objective function is the maximization of the J4 ratio. Tests were carried out on artificially generated classes, with several non-relevant features. Real data tests were also performed on segmentation of naildfold capillaroscopic images, and NIST-38 database (prototype selection).