Toward Large-Scale Continuous EDA: A Random Matrix Theory Perspective

Learning Local Feature Descriptors Using Convex Optimisation

(2014) Karen Simonyan et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions

(2014) Robert J. Durrant et al.   MACHINE LEARNING

Covariance estimation for distributions with ${2+\varepsilon}$ moments

(2013) Nikhil Srivastava et al.   ANNALS OF PROBABILITY

Scaling Up Estimation of Distribution Algorithms for Continuous Optimization

(2013) Weishan Dong et al.   IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

Benchmarking Parameter-Free AMaLGaM on Functions With and Without Noise

(2012) Peter A. N. Bosman et al.   EVOLUTIONARY COMPUTATION

Smolign: A Spatial Motifs-Based Protein Multiple Structural Alignment Method

(2012) Hong Sun et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

A Random Matrix-Theoretic Approach to Handling Singular Covariance Estimates

(2011) Thomas L. Marzetta et al.   IEEE TRANSACTIONS ON INFORMATION THEORY

How Close is the Sample Covariance Matrix to the Actual Covariance Matrix?

(2011) Roman Vershynin   JOURNAL OF THEORETICAL PROBABILITY

Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms

(2008) Weishan Dong et al.   INFORMATION SCIENCES