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Spectral Regularization Algorithms for Learning Large Incomplete Matrices
collaborative filtering nuclear norm spectral regularization netflix prize large scale convex optimization
2015/8/21
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we provide a simple and...
Two sample tests for high-dimensional covariance matrices
High-dimensional covariance large p small n likelihood ratio test testing for gene-sets
2012/6/21
We propose two tests for the equality of covariance matrices between two high-dimensional populations. One test is on the whole variance--covariance matrices, and the other is on off-diagonal sub-matr...
Estimation in high-dimensional linear models with deterministic design matrices
Identifiability projection ridge regression sparsity thresholding variable selection
2012/6/21
Because of the advance in technologies, modern statistical studies often encounter linear models with the number of explanatory variables much larger than the sample size. Estimation and variable sele...