Toward Large-Scale Continuous EDA: A Random Matrix Theory Perspective
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Ata Kabán | Jakramate Bootkrajang | Robert J. Durrant | A. Kabán | Jakramate Bootkrajang | R. Durrant
[1] R. Vershynin. How Close is the Sample Covariance Matrix to the Actual Covariance Matrix? , 2010, 1004.3484.
[2] U. Alon,et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[3] Ata Kabán,et al. Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions , 2015, Machine Learning.
[4] G. Lorentz,et al. Constructive approximation : advanced problems , 1996 .
[5] Heinz Mühlenbein,et al. Convergence Theory and Applications of the Factorized Distribution Algorithm , 2015, CIT 2015.
[6] Alex A. Freitas,et al. Evolutionary Computation , 2002 .
[7] Xin Yao,et al. Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms , 2008, Inf. Sci..
[8] Rudolf Ahlswede,et al. Strong converse for identification via quantum channels , 2000, IEEE Trans. Inf. Theory.
[9] Peter A. N. Bosman,et al. On empirical memory design, faster selection of bayesian factorizations and parameter-free gaussian EDAs , 2009, GECCO.
[10] Santosh S. Vempala,et al. The Random Projection Method , 2005, DIMACS Series in Discrete Mathematics and Theoretical Computer Science.
[11] Xin Yao,et al. On the approximation ability of evolutionary optimization with application to minimum set cover , 2010, Artif. Intell..
[12] Qingfu Zhang,et al. On the limits of effectiveness in estimation of distribution algorithms , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).
[13] R. Vershynin,et al. Covariance estimation for distributions with 2+ε moments , 2011, 1106.2775.
[14] Thomas L. Marzetta,et al. A Random Matrix-Theoretic Approach to Handling Singular Covariance Estimates , 2011, IEEE Transactions on Information Theory.
[15] Sanjoy Dasgupta,et al. Learning mixtures of Gaussians , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).
[16] Ata Kabán,et al. When is 'nearest neighbour' meaningful: A converse theorem and implications , 2009, J. Complex..
[17] Ponnuthurai Nagaratnam Suganthan,et al. Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization , 2008 .
[18] Hong Sun,et al. Smolign: A Spatial Motifs-Based Protein Multiple Structural Alignment Method , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[19] T. Apostol. Mathematical Analysis , 1957 .
[20] Raymond Ros,et al. A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity , 2008, PPSN.
[21] Xin Yao,et al. Large scale evolutionary optimization using cooperative coevolution , 2008, Inf. Sci..
[22] Dimitris Achlioptas,et al. Database-friendly random projections: Johnson-Lindenstrauss with binary coins , 2003, J. Comput. Syst. Sci..
[23] Roman Vershynin,et al. Introduction to the non-asymptotic analysis of random matrices , 2010, Compressed Sensing.
[24] James N. Knight,et al. Reducing the space-time complexity of the CMA-ES , 2007, GECCO '07.
[25] Ashutosh Kumar Singh,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .
[26] Ata Kabán. New Bounds on Compressive Linear Least Squares Regression , 2014, AISTATS.
[27] Andrew Zisserman,et al. Learning Local Feature Descriptors Using Convex Optimisation , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Xin Yao,et al. Multilevel cooperative coevolution for large scale optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).
[29] Ata Kabán,et al. Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions , 2013, ACML.
[30] X. Yao,et al. An analysis of evolutionary algorithms for finding approximation solutions to hard optimisation problems , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..
[31] Ata Kabán,et al. Classification of mislabelled microarrays using robust sparse logistic regression , 2013, Bioinform..
[32] J. A. Lozano,et al. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .
[33] J. S. Marron,et al. Geometric representation of high dimension, low sample size data , 2005 .
[34] D. Freedman,et al. Asymptotics of Graphical Projection Pursuit , 1984 .
[35] Peter Tiño,et al. Scaling Up Estimation of Distribution Algorithms for Continuous Optimization , 2011, IEEE Transactions on Evolutionary Computation.
[36] M. Rudelson,et al. Non-asymptotic theory of random matrices: extreme singular values , 2010, 1003.2990.
[37] Jonathan M. Garibaldi,et al. Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[38] Francisco Herrera,et al. Memetic algorithm with Local search chaining for large scale continuous optimization problems , 2009, 2009 IEEE Congress on Evolutionary Computation.
[39] Pedro Larrañaga,et al. Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.
[40] Dirk Thierens,et al. Benchmarking Parameter-Free AMaLGaM on Functions With and Without Noise , 2013, Evolutionary Computation.
[41] Michael W. Mahoney Boyd,et al. Randomized Algorithms for Matrices and Data , 2010 .
[42] Bin Li,et al. A restart univariate estimation of distribution algorithm: sampling under mixed Gaussian and Lévy probability distribution , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).
[43] Xiaodong Li,et al. A Comparative Study of CMA-ES on Large Scale Global Optimisation , 2010, Australasian Conference on Artificial Intelligence.
[44] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[45] Nikolaus Hansen,et al. The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.
[46] Francisco Herrera,et al. MA-SW-Chains: Memetic algorithm based on local search chains for large scale continuous global optimization , 2010, IEEE Congress on Evolutionary Computation.
[47] Jonathan Goldstein,et al. When Is ''Nearest Neighbor'' Meaningful? , 1999, ICDT.