Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-class svms for document classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Learning from Unbalanced Data
Applied Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Incremental tensor analysis: Theory and applications
ACM Transactions on Knowledge Discovery from Data (TKDD)
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Asymmetric Principal Component and Discriminant Analyses for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
Discriminative orthogonal neighborhood-preserving projections for classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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In an imbalanced dataset, the positive and negative classes can be quite different in both size and distribution. This degrades the performance of many feature extraction methods and classifiers. This paper proposes a method for extracting minimum positive and maximum negative features (in terms of absolute value) for imbalanced binary classification. This paper develops two models to yield the feature extractors. Model 1 first generates a set of candidate extractors that can minimize the positive features to be zero, and then chooses the ones among these candidates that can maximize the negative features. Model 2 first generates a set of candidate extractors that can maximize the negative features, and then chooses the ones that can minimize the positive features. Compared with the traditional feature extraction methods and classifiers, the proposed models are less likely affected by the imbalance of the dataset. Experimental results show that these models can perform well when the positive class and negative class are imbalanced in both size and distribution.