Schulz A, Hammer B. Discriminative dimensionality reduction for regression problems using the Fisher metric. In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE); 2015: 1-8
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
Distance-based methods in machine learning and pattern recognition have to rely on a metric distance...
Gisbrecht A, Schulz A, Hammer B. Discriminative Dimensionality Reduction for the Visualization of Cl...
Schulz A, Brinkrolf J, Hammer B. Efficient Kernelization of Discriminative Dimensionality Reduction....
Schulz A, Hammer B. Metric Learning in Dimensionality Reduction. In: Proceedings of the Internation...
Abstract. Fisher criterion has achieved great success in dimensional-ity reduction. Two representati...
Hammer B, Gisbrecht A, Schulz A. Applications of discriminative dimensionality reduction. In: Proce...
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for...
Gisbrecht A, Hofmann D, Hammer B. Discriminative Dimensionality Reduction Mappings. In: Hollmén J, K...
This work considers the applicability of applying the derivatives of stepwise linear regression mode...
The Fisher linear discriminant analysis (LDA) is a classical method for classification and dimen-sio...
Schulz A, Gisbrecht A, Hammer B. Classifier inspection based on different discriminative dimensional...
Knowledge discovery from big data demands effective representation of data. However, big data are of...
Well known linear discriminant analysis (LDA) based on the Fisher criterion is incapable of dealing ...
Schulz A, Hammer B. Discriminative Dimensionality Reduction in Kernel Space. In: ESANN2016 Proceedi...
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
Distance-based methods in machine learning and pattern recognition have to rely on a metric distance...
Gisbrecht A, Schulz A, Hammer B. Discriminative Dimensionality Reduction for the Visualization of Cl...
Schulz A, Brinkrolf J, Hammer B. Efficient Kernelization of Discriminative Dimensionality Reduction....
Schulz A, Hammer B. Metric Learning in Dimensionality Reduction. In: Proceedings of the Internation...
Abstract. Fisher criterion has achieved great success in dimensional-ity reduction. Two representati...
Hammer B, Gisbrecht A, Schulz A. Applications of discriminative dimensionality reduction. In: Proce...
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for...
Gisbrecht A, Hofmann D, Hammer B. Discriminative Dimensionality Reduction Mappings. In: Hollmén J, K...
This work considers the applicability of applying the derivatives of stepwise linear regression mode...
The Fisher linear discriminant analysis (LDA) is a classical method for classification and dimen-sio...
Schulz A, Gisbrecht A, Hammer B. Classifier inspection based on different discriminative dimensional...
Knowledge discovery from big data demands effective representation of data. However, big data are of...
Well known linear discriminant analysis (LDA) based on the Fisher criterion is incapable of dealing ...
Schulz A, Hammer B. Discriminative Dimensionality Reduction in Kernel Space. In: ESANN2016 Proceedi...
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
Distance-based methods in machine learning and pattern recognition have to rely on a metric distance...
Gisbrecht A, Schulz A, Hammer B. Discriminative Dimensionality Reduction for the Visualization of Cl...
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