2009/12/11 by Hua Zhou, David Alexander, David H. Alexander +1 · 1 citation
Computer Science · Engineering · #Blind Source Separation Techniques #Direction-of-Arrival Estimation Techniques #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.1007/s11222-009-9166-3
openalex publication_date 2009/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
In many statistical problems, maximum likelihood estimation by an EM or MM algorithm suffers from excruciatingly slow convergence. This tendency limits the application of these algorithms to modern high-dimensional problems in data mining, genomics, and imaging. Unfortunately, most existing acceleration techniques are ill-suited to complicated models involving large numbers of parameters. The squared iterative methods (SQUAREM) recently proposed by Varadhan and Roland constitute one notable exception. This paper presents a new quasi-Newton acceleration scheme that requires only modest increments in computation per iteration and overall storage and rivals or surpasses the performance of SQUAREM on several representative test problems.