Supervised dimensionality reduction
15 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional Autoencoders
johnnellas/csae
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It turned out that this methodology can also be greatly beneficial in enforcing explainability of deep learning architectures.
Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A Human
bghojogh/Affective-Manifold
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More affective manifolds in the machine's mind can make it more realistic and effective.
Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General Relativity
Due to the effectiveness of using machine learning in physics, it has been widely received increased attention in the literature.
Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks
dannyzx/grbf-nns
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Providing a model that achieves a strong predictive performance and at the same time is interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives.
Curvature Augmented Manifold Embedding and Learning
A new dimensional reduction (DR) and data visualization method, Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed.
