456ac9b0d15a8b7f1e71073221059886-Reviews.html

"NIPS 2013 Neural Information Processing Systems December 5 - 10, Lake Tahoe, Nevada, USA",,, "Paper ID:","1051" "Title:","Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation" Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper studies the problem of identifying Gaussians in a mixture in high dimensions when the separation between the Gaussians is small. The assumption is that the Gaussians are separated along few dimensions and hence by identifying these dimensions, that is, feature selection, the curse of dimensionality can be bitten and the Gaussians can be found. Clustering in high dimension is an open problem that well deserve a study. The theoretical approach taken by the authors is good step in the path towards better understanding the problem.