Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
A maximum entropy approach to species distribution modeling
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Triple Jump Acceleration for the EM Algorithm
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Convergence Theorems for Generalized Alternating Minimization Procedures
The Journal of Machine Learning Research
Journal of VLSI Signal Processing Systems
Algorithms for sparse nonnegative tucker decompositions
Neural Computation
Data Mining and Knowledge Discovery
Best document selection based on approximate utility optimization
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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Many practitioners who use EM and related algorithms complain that they are sometimes slow. When does this happen, and what can be done about it? In this paper, we study the general class of bound optimization algorithms - including EM, Iterative Scaling, Non-negative Matrix Factorization, CCCP - and their relationship to direct optimization algorithms such as gradientbased methods for parameter learning. We derive a general relationship between the updates performed by bound optimization methods and those of gradient and second-order methods and identify analytic conditions under which bound optimization algorithms exhibit quasi-Newton behavior, and under which they possess poor, first-order convergence. Based on this analysis, we consider several specific algorithms, interpret and analyze their convergence properties and provide some recipes for preprocessing input to these algorithms to yield faster convergence behavior. We report empirical results supporting our analysis and showing that simple data preprocessing can result in dramatically improved performance of bound optimizers in practice.