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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.