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. CarreiraPerpinan, 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 treebased variable importances to selectively identify relevant variables Van Anh HuynhThu, Louis Wehenkel, Pierre Geurts 
Fast principal component analysis using fixedpoint 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 
tDistributed Stochastic Neighbor Embedding  a technique for dimensionality reduction that is particularly well suited for the visualization of highdimensional datasets.
