A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement

A smoothing majorization method for l22-lpp matrix minimization

We discuss the l2-lp (with p ∈ (0, 1)) matrix minimization for recovering low rank matrix. A smoothing approach is developed for solving this non-smooth, non-Lipschitz and non-convex optimization problem, in which the smoothing parameter is used as a variable and a majorization method is adopted to solve the smoothing problem. The convergence theorem shows that any accumulation point of the seq...

On Nonlinear Preservers of Weak Matrix Majorization

Majorization-Minimization for Manifold Embedding

Nonlinear dimensionality reduction by manifold embedding has become a popular and powerful approach both for visualization and as preprocessing for predictive tasks, but more efficient optimization algorithms are still crucially needed. MajorizationMinimization (MM) is a promising approach that monotonically decreases the cost function, but it remains unknown how to tightly majorize the manifol...

Generalized Majorization-Minimization

Non-convex optimization is ubiquitous in machine learning. The MajorizationMinimization (MM) procedure systematically optimizes non-convex functions through an iterative construction and optimization of upper bounds on the objective function. The bound at each iteration is required to touch the objective function at the optimizer of the previous bound. We show that this touching constraint is u...

Ontology Similarity Measuring and Ontology Mapping Algorithms Based on Majorization-Minimization Method

Ontology similarity calculation is important research topics in information retrieval and widely used in science and engineering. In information retrieval, ontology is aiming to find the highly semantic similarity information of the original query concept, and then return the results to the user. Ontology mapping is aiming toconstruct the relationship between two or more ontologies. The core tr...