Combining overlapping information
Management Science
Latent variable models and factors analysis
Latent variable models and factors analysis
Operations Research
Probabilistic inference and influence diagrams
Operations Research
Management Science
Aggregating point estimates: a flexible modeling approach
Management Science
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Linear redundancy reduction learning
Neural Networks
A fast fixed-point algorithm for independent component analysis
Neural Computation
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Hierarchical non-linear factor analysis and topographic maps
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Key concepts in model selection: performance and generalizability
Journal of Mathematical Psychology
Independent component analysis: algorithms and applications
Neural Networks
A class of neural networks for independent component analysis
IEEE Transactions on Neural Networks
Acoustic factor analysis for streamed hidden Markov modeling
IEEE Transactions on Audio, Speech, and Language Processing
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Model selection and model combination is a general problem in many areas. Especially, when we have several different candidate models and also have gathered a new data set, we want to construct a more accurate and precise model in order to help predict future events. In this paper, we propose a new data-guided model combination method by decomposition and aggregation. With the aid of influence diagrams, we analyze the dependence among candidate models and apply latent factors to characterize such dependence. After analyzing model structures in this framework, we derive an optimal composite model. Two widely used data analysis tools, namely, Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are applied for the purpose of factor extraction from the class of candidate models. Once factors are ready, they are sorted and aggregated in order to produce composite models. During the course of factor aggregation, another important issue, namely factor selection, is also touched on. Finally, a numerical study shows how this method works and an application using physical data is also presented.