Hard thresholdingEmpirical riskPenalizationOracle inequality62G05This paper focuses on computing a nearly optimal penalty in the method of empirical risk minimization. It is assumed that we have at our disposal the noisy data Y=θ +σξ, where [FORMULA] is an unknown vector and [FORMULA] is a...
Image Segmentation by Multi-level Thresholding Based on C-Means Clustering Algorithms and Fuzzy Entropy C-means clustering algorithms (hard C-means clustering algorithm and fuzzy C-means clustering algorithm) are common thresholding segmentation techniques. T... F Zhao,J Fan - International Conference ...
We study a class of constrained sparse optimization problems with cardinality penalty, where the feasible set is defined by box constraint, and the loss function is convex but not necessarily smooth. First, we propose an accelerated smoothing hard thresholding (ASHT) algorithm for solving such ...