@ARTICLE{6990594, 
author={M. Chen and I. W. Tsang and M. Tan and T. J. Cham}, 
journal={IEEE Transactions on Knowledge and Data Engineering}, 
title={A Unified Feature Selection Framework for Graph Embedding on High Dimensional Data}, 
year={2015}, 
volume={27}, 
number={6}, 
pages={1465-1477}, 
keywords={convex programming;feature selection;gradient methods;graph theory;learning (artificial intelligence);least squares approximations;quadratic programming;regression analysis;unified feature selection framework;high dimensional data;data structure discovery;least squares regression problem;binary feature selector;integral programming problem;convex quadratically constrained quadratic program learning problem;convex QCQP learning problem;accelerated proximal gradient methods;APG methods;APG optimization;graph embedding learning problems;Optimization;Vectors;Principal component analysis;Data structures;Educational institutions;Sparse matrices;Gain measurement;Sparse graph embedding;sparse principal component analysis;efficient feature selection;high dimensional data;Sparse graph embedding;sparse principal component analysis;efficient feature selection;high dimensional data}, 
doi={10.1109/TKDE.2014.2382599}, 
ISSN={1041-4347}, 
month={June},}