Name
Abstract | ||
|---|---|---|
Addressing the problem of detecting the number of source signals as selecting the hidden dimensionality of Factor Analysis (FA) model, we investigate several model selection criteria via a new empirical analyzing tool that examines the joint effect of signal-noise ratio (SNR) and sample size N on the model selection performance. The contours of the model selection accuracies visualize a three-region partition on the space of SNR and N, and a diminishing marginal effect which trades off SNR and N on the performance. Moreover, the newly derived Variational Bayes algorithm and three variants of Bayesian Ying-Yang (BYY) algorithms are more robust against reducing SNR and N, where the BYY with priors' hyperparameters updated is the best in general. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICASSP.2010.5495287 | 2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING |
Keywords | Field | DocType |
Number of signals, hidden dimensionality, linear model, model selection, criteria | Pattern recognition,Hyperparameter,Linear model,Computer science,Model selection,Curse of dimensionality,Artificial intelligence,Covariance matrix,Prior probability,Bayes' theorem,Bayesian probability | Conference |
ISSN | Citations | PageRank |
1520-6149 | 1 | 0.35 |
References | Authors | |
4 | 2 | |