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Statistical Significance of Clustering using Soft Thresholding
Covariance Estimation High Dimension Invariance Principles Unsupervised Learning
2013/6/14
Clustering methods have led to a number of important discoveries in bioinformatics and beyond. A major challenge in their use is determining which clusters represent important underlying structure, as...
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
A General Iterative Shrinkage Thresholding Algorithm Non-convex Regularized Optimization Problems
2013/5/2
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterp...
Subspace Clustering via Thresholding and Spectral Clustering
Subspace Clustering Thresholding Spectral Clustering
2013/5/2
We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown...
Sparse linear discriminant analysis by thresholding for high dimensional data
Classification high dimensionality misclassification rate nor-mality optimal classification rule sparse estimates
2011/6/20
In many social, economical, biological and medical studies, one
objective is to classify a subject into one of several classes based on
a set of variables observed from the subject. Because the prob...