| An Introduction to Variable and Feature Selection Isabelle Guyon, Andre Elisseeff |
| A comparative study on feature selection in text categorization Yiming Yang and Jan O Pedersen |
| A fast, universal algorithm to learn parametric nonlinear embeddings Miguel A. Carreira-Perpinan, Max Vladymyrov |
| Anomaly detection: A survey Varun Chandola, Arindam Banerjee, Vipin Kumar |
| Dimensionality Reduction of Massive Sparse Datasets Using Coresets Dan Feldman, Mikhail Volkov, Daniela Rus |
| Experiments with Random Projection Sanjoy Dasgupta |
| Exploiting tree-based variable importances to selectively identify relevant variables Van Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts |
| Fast principal component analysis using fixed-point algorithm Alok Sharma, Kuldip K. Paliwal |
| Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination Eugene Tuv, Alexander Borisov, George Runger, Kari Torkkola |
| Geometric Methods for Feature Extraction and Dimensional Reduction: A Guided Tour Christopher J.C. Burges - e a tutorial overview of several geometric methods for feature selection and dimensional reduction.
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| ISOMAP - A global geometric framework for nonlinear dimensionality reduction
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| JMLR Special Topic on Causality |
| Random projection in dimensionality reduction: Applications to image and text data Ella Bingham, Heikki Mannila |
| Random projection trees for vector quantization Sanjoy Dasgupta, Yoav Freund |
| Streamwise Feature Selection Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H. Ungar |
| t-Distributed Stochastic Neighbor Embedding - a technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.
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| t-Distributed Stochastic Neighbor Embedding (t-SNE) - a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.
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| UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction Leland McInnes, John Healy |
