| 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.
|ISOMAP - A global geometric framework for nonlinear dimensionality reduction
|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.
|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.
|UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction Leland McInnes, John Healy