Supervised dimensionality reduction
15 papers with code • 0 benchmarks • 0 datasets
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Latest papers
Curvature Augmented Manifold Embedding and Learning
A new dimensional reduction (DR) and data visualization method, Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed.
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.
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.
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.
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.
SLISEMAP: Supervised dimensionality reduction through local explanations
Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item.
Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM
Dimensionality reduction methods have found vast application as visualization tools in diverse areas of science.
Computer-aided Interpretable Features for Leaf Image Classification
The main image processing steps of our algorithm involves: i) Convert original image to RGB (Red-Green-Blue) image, ii) Gray scaling, iii) Gaussian smoothing, iv) Binary thresholding, v) Remove stalk, vi) Closing holes, and vii) Resize image.
Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks
We present a dimensionality reduction network (MMINet) training procedure based on the stochastic estimate of the mutual information gradient.
Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN features
The method finds the new classes close to the corresponding standard classes we took the data form.
