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Found 7 papers, 0 papers with code
Subspace Learning from Extremely Compressed Measurements
Our theoretical results show that even a constant number of measurements per column suffices to approximate the principal subspace to arbitrary precision, provided that the number of vectors is large.
Extreme Compressive Sampling for Covariance Estimation
This paper studies the problem of estimating the covariance of a collection of vectors using only highly compressed measurements of each vector.
Feature Selection For High-Dimensional Clustering
We provide explicit bounds on the error rate of the resulting clustering.
Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures
We consider the problem of clustering data points in high dimensions, i. e. when the number of data points may be much smaller than the number of dimensions.
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation
While several papers have investigated computationally and statistically efficient methods for learning Gaussian mixtures, precise minimax bounds for their statistical performance as well as fundamental limits in high-dimensional settings are not well-understood.
Density-sensitive semisupervised inference
Semisupervised methods are techniques for using labeled data $(X_1, Y_1),\ldots,(X_n, Y_n)$ together with unlabeled data $X_{n+1},\ldots, X_N$ to make predictions.
