Supervised exponential family principal component analysis via convex optimization
Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels are often available and indicate important underlying structure in the data. In this paper, we present a novel convex supervised dimensionality reduction approach based on exponential family PCA, which is able to avoid the local optima of typical EM learning. Moreover, by introducing a sample-based approximation to exponential family models, it overcomes the limitation of the...[Show more]
|Collections||ANU Research Publications|
|Source:||Advances in Neural Information Processing Systems 21|
|01_Guo_Supervised_exponential_family_2008.pdf||251.19 kB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.